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Best Stock Market APIs for Python Developers in 2026

TheBitForge — Wed, 28 Oct 2026 18:07:11 +0000

If your Python project is built on company numbers rather than ticking prices, start with ROIC.ai. That is my short answer, and the rest of this guide explains why, where it falls short, and which other stock market APIs make more sense when your project looks different.

Almost everyone starts the same way, by the way. Three lines with a popular library, a neat table in the terminal, one proud afternoon. Then the script runs on a schedule, an unofficial endpoint changes, and the dashboard is dead on a Monday morning. This guide is for the day after that.

Quick picks 🏆

Fundamentals, ratios, transcripts: ROIC.ai (top pick)
Live US data and options: Massive
Trading bots: Alpaca
Global history: EODHD
Learning on a free key: Alpha Vantage or Twelve Data
Free live US quotes: Finnhub
Personal notebooks only: yfinance

💡 Tip: Pricing in this market moves without warning. Everything here was checked against public pages and recent developer write ups in October 2026. Confirm on the provider's own page before you add a card.

ROIC.ai, the top pick
Scorecard at a glance
The other APIs
How to choose
Mistakes that cost time
FAQ

ROIC.ai, the top pick for fundamentals driven projects

Website: roic.ai and its API docs

Most stock APIs are really price APIs with fundamentals bolted on later. ROIC.ai is built the other way round. The core product is standardized company data: income statements, balance sheets, cash flow, more than 90 pre calculated ratios, valuation multiples and earnings call transcripts, all behind one consistent JSON format.

The published scale is roughly 68,000 tickers across more than 70 exchanges, up to 40 years of statements depending on the plan, 120 plus quarters per company, over 17 years of call transcripts, and daily forex rates for more than 4,000 currency pairs. It also has company profiles, logos, sector and industry reference data, stock splits and an earnings calendar with estimates and surprises.

Why Python developers tend to like it:

Ratios arrive pre computed. You do not have to rebuild return on invested capital from raw statements. The idea behind the name is simple.

RO I C = \frac{NOP A T}{I n v es t e d C a p i t a l}

One format everywhere. Resources, lists and errors share the same JSON shapes, so once you integrate statements, the same habits work for prices and transcripts.
An official Python SDK, with Node and Go versions too. The package works with pandas and polars.
Transcripts in the same API. Prepared remarks and Q and A organized by fiscal quarter, which is rare at this price.
A hosted MCP server, so assistants like Claude or Cursor can query the same data in plain language. That matters if you build AI research tools.
Easy to try. The free plan needs no credit card.

A first call takes a minute:

# pip install roicai
from roicai import Roicai

client = Roicai(api_key="YOUR_API_KEY")  # or set ROICAI_API_KEY

statements = client.fundamentals.income_statements.list(
    "NASDAQ:AAPL",
    period_type="annual",
    limit=5,
)
for year in statements.items:
    print(year.fiscal_year, year.is_sales_revenue_turnover, year.is_net_income)

Prefer plain REST? The identifier is just exchange, colon, ticker:

curl "https://api.roic.ai/v3.0.0/stock-prices/latest/NASDAQ:AAPL?apikey=YOUR_API_KEY"

and the response is clean, predictable JSON:

{
  "data": [
    {
      "date": "2026-07-17",
      "open": 210.61,
      "high": 213.34,
      "low": 209.85,
      "close": 212.41,
      "volume": 48923700
    }
  ],
  "symbol": "NASDAQ:AAPL",
  "currency": "USD"
}

Plans and limits in short
The free plan allows 5 requests per minute with about two years of annual fundamentals and daily prices, and no card. Paid plans start at 29 USD per month with higher limits, and the vendor lists 300 requests per minute at that level. Bulk downloads and some endpoints need a paid plan, and commercial redistribution is handled through an Enterprise plan. Check the pricing page for current tiers.

⚠️ The honest part: ROIC.ai serves daily price data, not streaming quotes¹. If you need a live ticker or tick data, pair it with a real time provider from the list below. For screeners, valuation models, research dashboards and anything that asks how a business is really doing, it is the one I would start with.

roicai / roicai-python

Official Python SDK for the ROIC.ai financial data and stock API: fundamentals, earnings call transcripts, stock prices, pandas and polars

ROIC.AI Python Library

The ROIC.AI Python library is the official Python client for the ROIC.AI financial data API and stock API. Use it to get fundamentals for public companies, such as income statements, balance sheets, cash flow statements, financial ratios and valuation multiples, and earnings call transcripts. It also covers historical and latest stock prices, SEC filings and other company documents, the earnings calendar, company profiles and forex rates.

Responses are typed Pydantic models, list methods fetch the next pages for you, and any response converts to a pandas or polars DataFrame.

Documentation

See the ROIC.AI API reference for every endpoint, parameter and field, and the API guides for authentication, plans and response formats. reference.md lists every method of this library with its parameters.

Requirements

Python 3.10 or later.

Installation

Install the package with:

pip install --upgrade roicai

To convert responses to DataFrames, install pandas or polars with it:

pip

…

View on GitHub

Get a free ROIC.ai API key

Scorecard at a glance

API	Best at	Free tier	First paid plan	Python route
ROIC.ai	Fundamentals, transcripts	5 requests per minute	29 USD per month	Official SDK
Alpha Vantage	Learning, indicators	25 requests per day	49.99 USD per month	requests
Massive (ex Polygon.io)	Live US, options	5 calls per minute	about 29 USD per month	Official client
EODHD	Global history	about 20 calls per day	about 19.99 per month	Official library
Financial Modeling Prep	US fundamentals	about 250 calls per day	about 22 USD per month	requests
Twelve Data	Many asset types	about 800 requests per day	see pricing page	Official client
Finnhub	Free live US quotes	about 60 calls per minute	about 49.99 USD per month	Official client
Alpaca	Trading bots	Free, IEX feed	about 99 USD per month	alpaca py
Tiingo	Clean price history	about 1,000 requests per day	about 30 USD per month	Official client
yfinance	Personal scripts	Free, unofficial	None	pip install yfinance

The other APIs, and when they win

Before comparing, decide what data you really need. Stock data is four needs under one name: end of day prices, real time streams, fundamentals, and the extras around them such as transcripts and filings. Most bad choices come from skipping this step.

Alpha Vantage, the easy first stop

Website: alphavantage.co

A huge number of tutorials begin here. A free key arrives in a minute, and one API covers stocks, forex, crypto, fundamentals and many technical indicators. The catch is the free limit of 25 requests per day and 5 per minute. Premium plans are priced by requests per minute, from 49.99 USD for 75 per minute up to 249.99 USD for 1,200, with no daily cap.

import os
import requests

params = {
    "function": "TIME_SERIES_DAILY",
    "symbol": "IBM",
    "apikey": os.environ["ALPHAVANTAGE_API_KEY"],
}
data = requests.get("https://www.alphavantage.co/query", params=params, timeout=30).json()
latest_day = max(data["Time Series (Daily)"])
print(latest_day, data["Time Series (Daily)"][latest_day]["4. close"])

Best for: learning and prototypes. Watch out for: the jump from free to 49.99 USD.

Massive, formerly Polygon.io

Website: massive.com

Same company, rebranded on October 30, 2025, with existing keys still working. Built around US market infrastructure with REST and WebSocket access, trades, quotes, aggregates and deep options coverage. The free plan gives 5 calls per minute of end of day data. Individual stock plans sit around 29, 79 and 199 USD per month, with options, forex and crypto priced separately.

from massive import RESTClient

client = RESTClient(api_key="YOUR_KEY")

for bar in client.list_aggs("AAPL", 1, "day", "2026-09-01", "2026-09-30"):
    print(bar)

Best for: live and intraday US data. Watch out for: paying per asset class.

EODHD, for global historical data

Website: eodhd.com

Advertises more than 30 years of history, 60 plus exchanges and over 150,000 tickers, with fundamentals and some real time data under one key. The free plan is closer to a trial at about 20 calls per day. Paid plans start near 19.99 per month, among the cheapest for worldwide coverage. Not built for fast intraday systems.

Financial Modeling Prep, for US fundamentals

Website: financialmodelingprep.com

Popular for statements and screening, with about 250 free calls per day of end of day data. The Starter plan is listed near 22 USD per month billed annually, but it is US focused and global data sits in higher tiers.

Twelve Data, many asset types in one place

Website: twelvedata.com

Stocks, ETFs, forex, crypto and indices with a clean shared schema. Free gives about 800 requests per day, delayed data, and generally non commercial use, so read the licence.

Finnhub, generous free live US quotes

Website: finnhub.io

Around 60 free calls per minute, real time US quotes with limits, news, WebSockets and extras like social sentiment. Paid plans start near 49.99 USD per month. Caps vary by endpoint, so read the docs.

import finnhub

client = finnhub.Client(api_key="YOUR_KEY")
print(client.quote("AAPL"))

Alpaca, when your code also trades

Website: alpaca.markets

A broker first, with market data and an order API on one account plus a paper trading sandbox. The free feed is IEX only, so prices can differ from the full tape, and the full feed is listed near 99 USD per month. The Python library is alpaca py.

Tiingo, clean historical prices

Website: tiingo.com

Tidy, error checked end of day data with decades of history. Free allows about a thousand requests per day, and paid plans start near 30 USD per month. Quietly good for backtests.

yfinance, free with an asterisk

Website: github.com/ranaroussi/yfinance

An open source library that reads the same unofficial endpoints Yahoo uses for its own site. Free, no key, a few lines of code:

import yfinance as yf

hist = yf.Ticker("AAPL").history(period="1mo")
print(hist.tail())

The catch is the one we opened with: endpoints change without notice, heavy use triggers rate limits, there is no service agreement, and the project points to Yahoo's terms and describes the data as for personal, research and educational use. Fine for your notebook, a poor base for a product.

ranaroussi / yfinance

Download market data from Yahoo! Finance's API

yfinance offers a Pythonic way to fetch financial & market data from Yahoo!Ⓡ finance.

Important

Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.

yfinance is not affiliated, endorsed, or vetted by Yahoo, Inc. It's an open-source tool that uses Yahoo's publicly available APIs, and is intended for research and educational purposes.

You should refer to Yahoo!'s terms of use (here, here, and here) **for details on your rights to use the actual data downloaded.

Remember - the Yahoo! finance API is intended for personal use only.**

Tip

THE NEW DOCUMENTATION WEBSITE IS NOW LIVE! 🤘

Visit ranaroussi.github.io/yfinance

Main components

Ticker: single ticker data
Tickers: multiple tickers' data
download: download market data for multiple tickers
Market: get information about a market
WebSocket and AsyncWebSocket: live streaming data
Search: quotes and…

View on GitHub

Also worth knowing: Nasdaq Data Link for research datasets, Intrinio for standardized US fundamentals, Marketstack for cheap end of day data, and Bloomberg or QuoteMedia for teams that need licensed redistribution.

How to choose

Your project	Start with
Screener, valuation tool, research dashboard	ROIC.ai
AI agent that reads financials and transcripts	ROIC.ai with its MCP server
Live US dashboard or options	Massive
Trading bot	Alpaca
Global backtests	EODHD
Learning project	Alpha Vantage or Twelve Data
Private notebook	yfinance

Two providers is normal. A common and sensible pair is ROIC.ai for the fundamentals and a real time source for the prices, which usually costs less than one vendor trying to do everything.

Mistakes that cost developers time

Calling the API on every page load. Fetch once, store, refresh on a schedule. A tiny cache fixes most rate limit pain:

import json
import time
from pathlib import Path

def cached(key, fetch, ttl=3600):
    path = Path(f".cache/{key}.json")
    if path.exists() and time.time() - path.stat().st_mtime < ttl:
        return json.loads(path.read_text())
    data = fetch()
    path.parent.mkdir(exist_ok=True)
    path.write_text(json.dumps(data))
    return data

Hard coding one provider everywhere. Keep data calls in one module so a provider swap is a one line change:

- from providers.alphavantage import get_daily
+ from providers.roicai import get_daily

Ignoring adjusted prices. Splits and dividends change history, and an unadjusted backtest quietly produces nonsense.

Forgetting the licence. Free plans are often personal or non commercial only.

Trusting one source. Spot check a few values against a second source after any plan change or rebrand.

FAQ

Is there a completely free stock market API for Python?
Several have usable free tiers, including ROIC.ai, Alpha Vantage, Twelve Data, Finnhub, Financial Modeling Prep and Alpaca. All limit requests, history or freshness, so free works best for learning and small tools.

Which API is best for company fundamentals?
ROIC.ai, Financial Modeling Prep and Intrinio are the usual picks. ROIC.ai stands out when you want global coverage, pre calculated ratios and earnings call transcripts in one consistent format.

What is the best stock API for real time data?
For US stocks, Massive and Finnhub are the usual names, and Alpaca suits you if you also want to trade. Free real time is limited, so check delays and coverage.

Is yfinance safe for production?
No. It relies on unofficial endpoints with no guarantees, and Yahoo's terms point to personal use. Keep it for your own notebooks.

Can I use these APIs in a commercial product?
Sometimes, but rarely on a free plan. Check each licence, because redistribution rights usually sit in paid or enterprise tiers.

Final thoughts

There is no perfect stock API, only the one that fits what you are building this month. If the question your project asks is how good a company is, start with ROIC.ai. If it asks what the price is right now, start with a real time provider. Cache your data, keep the provider behind a small wrapper, and you can change your mind without a rewrite.

Which API are you using right now, and why did you pick it? Tell me in the comments.

¹ Based on the vendor's published documentation, which lists daily open, high, low, close and volume data. Pricing and limits throughout were checked in October 2026 and may have changed.

Best Free APIs for Developers : The Complete, Categorized List

TheBitForge — Mon, 26 Oct 2026 17:10:15 +0000

You do not need a credit card or a big budget to build real software in 2026. This guide rounds up 100+ genuinely free APIs across AI, weather, maps, finance, auth, email, payments, images, news, and sports, organized by category, with real free tier limits, auth requirements, and honest notes on where "free" quietly turns into "paid."

What Actually Counts as a Free API in 2026
Quick Picks by Use Case
AI and Machine Learning APIs
Weather APIs
Maps and Geolocation APIs
Finance, Stocks and Crypto APIs
Authentication and Identity APIs
Email and Messaging APIs
Payments APIs
Images, Video and Media APIs
News and Content APIs
Sports APIs
Public and Open Government Data APIs
Developer Utility APIs
API Testing and Documentation Tools
How to Pick a Free API Without Getting Burned
FAQs: Free APIs for Developers

What Actually Counts as a Free API in 2026

Before the list, a quick reality check, because the word "free" gets abused constantly in this space.

Free API offers generally come in three shapes.

No key, no auth, free forever. Open data, no signup, no card. The friendliest tier that exists. Examples: Open-Meteo, OpenStreetMap and Nominatim.

Free tier, permanent. Requires an API key, but the free quota resets every month or day and never expires on its own. Examples: Groq, Resend, Supabase, ROIC.ai.

Free trial or credit based. You get a time boxed credit balance, then it is gone. Often disguised as "free." Common with many AI model APIs and some mapping platforms.

The safest free APIs to build a real project on are the first two types. Trial credit APIs are fine for prototyping, but do not ship a production feature on top of one unless you have checked the pricing page the same week you deploy. Free tier terms change without notice, and "free forever" quietly becomes "free for 30 days" more often than vendors like to admit.

A genuinely good free API, in practice, should give you the following.

No credit card required to get your first API key
A clear, published rate limit, not fair use vagueness
Real documentation with working code samples
A free tier that scales with your project instead of expiring ahead of it

Keep that list in mind as you go. It will save you from building on sand.

Quick Picks by Use Case

If you need	Start with	Why
Weather data	Open-Meteo	No key, no signup, covers forecast plus historical plus air quality
Fast LLM inference	Groq	14,400 requests a day free, no card
Geocoding or maps	OpenStreetMap (Nominatim)	Free, open, no Google lock in
Transactional email	Resend	Clean API, great docs, generous free cap
Auth and user management	Supabase Auth	Full backend plus auth, free Postgres included
Payments	Stripe	Free to integrate, pay only on real transactions
Company financials and stock ratios	ROIC.ai	Free key, full endpoint access across 70+ exchanges
Stock photos	Unsplash or Pexels	No attribution required, commercial use OK
IP geolocation	ipinfo.io	50,000 requests a month free

AI and Machine Learning APIs

AI free tiers move faster than any other category on this list. Treat this section as a discovery map and double check limits before you build on one.

API	Free tier	Auth	Best for
Groq	14,400 requests a day, 30 requests a minute	API key, free, no card	Fast open model inference, Llama and Mixtral
Google Gemini API	Generous free quota for prototyping	API key	General purpose model with a clean SDK
OpenRouter	Rotating set of free suffixed models	No card, no phone	One API key, many models, easy comparison
Hugging Face Inference API	Free tier for model evaluation	API key	Thousands of open models, quick testing
Cloudflare Workers AI	Best free quota among edge AI providers	API key	Running inference close to your edge functions
GitHub Models	Free access inside your GitHub workflow	GitHub account	Trying frontier models without leaving your repo
AI Horde	Community powered, no account required	None	Image or text generation with zero signup friction
Ollama Cloud	Free on ramp, OpenAI compatible	No phone required	Self hosted style workflow without local GPU

Free LLM API limits change monthly. Before you depend on one for production, check the provider's pricing page the same week you ship, not the week you first signed up.

Weather APIs

Weather is one of the highest intent categories for developers because the free tier limit itself is usually the deciding factor.

API	Free tier	Auth	Best for
Open-Meteo	Unlimited for non commercial use, no key	None	The best free weather API with no key option: forecast, historical, air quality, marine, flood data
OpenWeatherMap	1,000 calls a day on the free Weather API plan	API key	Current weather, 5 day or 3 hour forecast, geocoding
Weatherstack	Free tier with real time data	API key	Simple real time plus historical weather lookups
Weather.gov (NWS)	Free, unlimited, US only	None	US government grade forecasts and alerts

OpenWeatherMap's free Weather API plan and its One Call 3.0 product are different offers with different quotas. Do not assume one covers the other.

Maps and Geolocation APIs

API	Free tier	Auth	Best for
OpenStreetMap and Nominatim	Free, open data geocoding	None	Forward and reverse geocoding without vendor lock in
Mapbox	Generous free monthly map loads	API key	Customizable vector maps, routing, navigation
Google Maps API	Free up to a monthly credit, card required	API key plus billing	Industry standard geocoding and Places data
OpenCage	Free tier for geocoding	API key	Clean, simple geocoding API with good docs
PositionStack	Free tier, forward and reverse geocoding	API key	Lightweight geocoding for smaller apps
ipinfo.io	50,000 requests a month free	Token for detailed data	IP geolocation, ASN lookup, timezone detection
ip-api.com	Free for non commercial use	None	Quick IP to location and timezone lookups
GeoJS	Free, no published rate limits	None	IP geolocation with minimal friction

Finance, Stocks and Crypto APIs

API	Free tier	Auth	Best for
ROIC.ai	Full API access on every plan, including free: 5 requests a minute and 2 years of history	API key, no credit card required	Pre calculated financial ratios (ROIC, ROE, debt to EBITDA), statements, valuation multiples, and earnings call transcripts across 70+ global exchanges, with JSON or spreadsheet ready TSV output
Alpha Vantage	Free API key, rate limited	API key	Real time plus historical equities and crypto data
Finnhub	Freemium, real time stock, forex, crypto plus news	API key	All in one market data with news sentiment
Twelve Data	Free tier for stock price lookups	API key	Simple, reliable stock price API
Polygon.io	Freemium	API key	Stocks, options, forex, and crypto market data
CoinGecko API	Free, generous public tier	None for basic use	Cryptocurrency prices, market cap, historical data
Frankfurter	Free, no key	None	Currency exchange rates and conversion
Marketaux	Free tier	API key	Financial news aggregation tied to tickers

ROIC.ai is worth a closer look if you are building anything that touches fundamentals. Unlike most "free tier" financial APIs that lock entire endpoint categories behind a paid plan, ROIC.ai gives every plan, including the free one, full access to all tickers and every endpoint. The difference between plans is just rate limit and history depth, so a side project can genuinely run on the free key without hitting a feature wall, only a request volume ceiling. The getting started docs walk you through your first call in a few minutes over a plain REST and JSON interface. It also ships a hosted MCP server, which is a clean way to wire company financials directly into an AI coding assistant or agent instead of writing a custom fetch layer by hand.

Authentication and Identity APIs

API	Free tier	Auth	Best for
Supabase Auth	Free with the full Supabase stack: Postgres, storage, edge functions	API key	Full backend as a service with real auth, not just tokens
Auth0	Free tier with published rate limits, for example 300 request burst on the Authentication API	Client ID and secret	Enterprise style auth, SSO, social login, on a budget
Clerk	Free tier for MAU based auth	API key	Drop in auth UI components for React and Next.js
Firebase Authentication	Free tier, generous for small apps	API key	Google ecosystem integration, quick setup

Email and Messaging APIs

Free email API limits vary widely by provider. Daily sending caps, inbound webhook support, and retention windows all differ, so match the provider to the job, not just the biggest number on the homepage.

API	Free tier	Auth	Best for
Resend	Solid monthly free cap, React Email support	API key	Modern transactional email with first class Next.js and React developer experience
Mailgun	Free tier available	API key	Reliable transactional plus marketing email
Amazon SES	Very low cost at scale, limited free allowance	AWS credentials	High volume sending once you outgrow smaller free tiers
Postmark	Free trial tier	API key	Fast, deliverability focused transactional email
Mailtrap	Free sandbox plus limited live sending	API key	Testing email flows before going to production
Cloudflare Email Workers	Free, usage based	API key	Lightweight inbound and outbound email routing on the edge
Twilio (SMS)	Free trial credit	API key	SMS and voice messaging for prototypes

Payments APIs

API	Free to integrate	Auth	Best for
Stripe	Yes, free until you process real transactions, then a per transaction fee	API key	The industry benchmark, full test mode for subscriptions, webhooks, refunds
PayPal API	Yes, pay per transaction	API key	Familiar checkout flow with broad consumer trust
Cashfree	Free integration, transaction based pricing	API key	Strong option for South Asian markets, UPI, cards, wallets
Lemon Squeezy	Free to integrate, revenue share on sales	API key	Merchant of record setup for indie SaaS and digital products

Payments APIs are free in the same way almost every entry here is. There is no cost to build the entire flow in test mode. You only pay once real money moves, and that is a per transaction cut, not a subscription.

Images, Video and Media APIs

API	Free tier	Auth	Best for
Unsplash API	Free, high request limit for demo apps	API key	High quality stock photography, no attribution required
Pexels API	Free, commercial use allowed	API key	Free stock photos and videos
Pixabay API	Free	API key	Images, illustrations, and video assets
Lorem Picsum	Free, no key	None	Instant placeholder and mockup images by size, ID, or seed
Cloudinary	Free tier for image and video storage plus transformation	API key	On the fly image resizing, optimization, and delivery
Imgur API	Free tier	API key	Image hosting and sharing

News and Content APIs

API	Free tier	Auth	Best for
NewsAPI.org	Free for development, limited for production	API key	Aggregated headlines across thousands of sources
World News API	Limited free quota	API key	Global news headlines in a simple JSON format
The Guardian Open Platform	Free, generous for non commercial use	API key	High quality journalism with full article access
NYT API	Free tier	API key	Archive search, article metadata, and more

Sports APIs

API	Free tier	Auth	Best for
TheSportsDB	Free, open	API key, free	Team, league, and player metadata
Football-Data.org	Free tier with rate limits	API key	Live scores and fixtures for major football leagues
API-Football	Free tier with daily request cap	API key	Detailed stats across global football leagues
balldontlie API	Free, no key for basic use	Optional key	NBA stats and historical game data

Public and Open Government Data APIs

API	Free tier	Auth	Best for
data.gov	Free, open	Varies by dataset	US government datasets across every sector
World Bank API	Free, open	None	Global economic and development indicators
REST Countries	Free, no key	None	Country metadata, flags, currencies, languages, borders
public-apis (GitHub)	Free directory, 1,700+ APIs across 50+ categories	Not applicable	The master list to discover niche free APIs this guide does not cover

Developer Utility APIs

Small, boring, and genuinely useful. The kind of API that saves you an afternoon of writing utility functions yourself.

API	Free tier	Auth	Best for
QR Server API	Free, no key	None	Generate QR codes on the fly
ipify	Free, no key	None	Get your public IP address in one call
JSONPlaceholder	Free, unlimited	None	Fake REST API for frontend prototyping and testing
AbstractAPI	Free suite of 15 REST APIs: email validation, screenshots, phone validation, and more	API key	Covering common boring utility needs in one place
ApyHub	Free tier across dozens of micro utilities	API key	File conversion, text processing, and similar one off tasks

API Testing and Documentation Tools

Not strictly APIs, but essential free tools for anyone building or consuming them.

Tool	Free tier	Best for
Apidog	Up to 4 users, unlimited projects and requests	All in one API design, mocking, and testing
Postman	Free tier for individuals and small teams	The standard API client for testing and collections
Beeceptor	Free, no code API mocking	Quickly mocking REST and GraphQL endpoints for testing
Swagger and OpenAPI tools	Free, open source	Auto generated interactive API documentation

How to Pick a Free API Without Getting Burned

Before you wire a free API into anything beyond a weekend project, run it through this short list.

No credit card required to get a working API key.
Published, specific rate limits, not vague fair use language.
Clear docs with real code samples, not just an endpoint list.
A changelog or status page showing the provider actively maintains it.
A free tier that scales with you, so you are not forced into a cliff edge upgrade.
Commercial use terms you have actually read, especially for anything client facing.

If an API fails two or more of these, it is fine for a prototype. Just do not build your startup's core feature on it without a backup plan.

Click to expand: FAQs about free APIs for developers

What is the best free API for weather data?

Open-Meteo is the strongest starting point in 2026. It requires no API key, has no hard rate limit for non commercial use, and covers current conditions, 7 day forecasts, historical weather, and air quality in one response.

What is the best free API for AI and LLM projects?

Groq currently offers one of the most generous genuinely free tiers, 14,400 requests a day, for fast LLM inference with no credit card required. OpenRouter is the best option if you want to compare multiple models through a single API key.

What is the best free API for financial data and stock ratios?

ROIC.ai stands out for anything that needs pre calculated ratios, financial statements, or earnings transcripts, because the free tier is not feature limited, only rate and history limited. Alpha Vantage and Finnhub are solid alternatives if your focus is raw price data rather than fundamentals.

Are free APIs good enough for production apps?

Yes, for low to moderate traffic. Many providers, including Resend, Supabase, Stripe, Open-Meteo, and ROIC.ai, run permanent free tiers that plenty of small to mid production apps never outgrow. The risk is not that free APIs are unreliable, it is that limits can change, so keep an eye on the provider's pricing page.

Do free APIs always require a credit card?

No. A growing number of providers, including Groq, OpenRouter, Open-Meteo, OpenStreetMap, Hugging Face, and ROIC.ai, let you get a working key with no card and, in several cases, no phone number either.

What is the difference between a free tier and a free trial?

A free tier is permanent. The same quota resets every billing cycle indefinitely. A free trial is time boxed or credit based and eventually runs out, even if the provider's marketing calls it free.

Where can I find more free APIs beyond this list?

The public-apis GitHub repository is the largest community maintained directory, listing 1,700+ free APIs across 50+ categories, and is a great next stop once you have exhausted this guide.

Closing Thoughts

The honest takeaway from researching this list is that there is more free API capacity available to developers in 2026 than most people realize. Between open data projects like Open-Meteo and OpenStreetMap, permanent free tiers from Groq, Resend, Supabase, and ROIC.ai, and pay only when you earn models like Stripe, you can genuinely build and ship a full stack product, AI features, auth, email, payments, financial data, and maps included, without spending a rupee until real users, and real revenue, show up.

Bookmark this page, keep a shortlist of two providers per category, and re verify limits before every major release. That one habit will save you more debugging time than any single API on this list.

If this list was useful, share it with a developer who is still paying for something that has a free alternative in here.

TheBitForge for Roic AI

Oct 23

101 Best Financial Data APIs in 2026

#api #programming #productivity #javascript

19 min read

Best Finance Data APIS

TheBitForge — Sun, 25 Oct 2026 20:40:26 +0000

If you are building anything around company fundamentals, start with ROIC.ai. It is a financial data API with prices, statements, ratios and earnings call transcripts for more than 68,000 tickers on over 70 exchanges, a free plan that needs no credit card, and a hosted MCP server so your AI tools can query the same data. It tops this list, and the first big section below explains why.

The rest of the post compares ROIC.ai with eight other providers, so you can see where it wins and where it does not. I looked at what actually decides a project: what data you get, how far back it goes, how fast you can pull it, what the free tier really allows, and whether you may ship the data inside a product.

A few honest notes first. I compared published documentation and pricing pages, last checked in late October 2026, and I did not run my own latency benchmarks. Prices move, so confirm every number on the vendor page. Near the end there is a small script you can use to measure the providers yourself.

The quick answer
Why ROIC.ai is my top pick
How to judge a finance data API
The comparison at a glance
The other best finance data APIs
Measure them yourself
Mistakes that cost real money
Frequently asked questions
Final thoughts

The quick answer

There is no single best finance data API for every job, and anyone who names one without asking what you are building is selling something. For fundamentals, ratios and transcripts across many markets, though, ROIC.ai is the one I would try first. For other jobs, here is where I would start.

If you need	Start with
Company fundamentals, ratios and transcripts across many countries	ROIC.ai
Statements and screening, US focused	Financial Modeling Prep
Live and streaming US market data	Massive, formerly Polygon.io
A free key to prototype with	Finnhub or Twelve Data
Decades of end of day history worldwide	EODHD
Clean adjusted prices for backtests	Tiingo
Official filings and raw XBRL facts at no cost	SEC EDGAR
A first project with plenty of tutorials	Alpha Vantage

Why ROIC.ai is my top pick

ROIC.ai is a financial data API covering stock prices, financial statements, earnings call transcripts, ratios and company data. Its published scale is more than 68,000 tickers across more than 70 global exchanges, with financial statement history going back over 40 years. Fundamentals come from SEC filings and prices from Nasdaq, refreshed daily, according to a third party review of the service.

Here is what makes it stand out.

Depth where it matters. Beyond income statements, balance sheets and cash flow, it exposes profitability ratios (including return on invested capital, the name on the door), credit and debt ratios, liquidity ratios with an Altman Z-Score, working capital ratios, yield analysis, enterprise value and valuation multiples. Most providers make you calculate those yourself.

A free plan you can really develop on. Worldwide coverage, a 5 requests per minute limit and the MCP server are all included at no cost. ROIC.ai's own comparison with Alpha Vantage points out that Alpha Vantage's free key stops at 25 requests per day, which is a very different experience for a developer testing an idea.

Real throughput on a small plan. The Individual plan is 29 dollars a month for 300 requests per minute, and Professional at 89 dollars a month has no request cap.

Earnings coverage. Earnings calendars with estimates and surprises, plus call transcripts in raw and edited versions, with audio and slide decks where available.

Built for AI tools. The hosted MCP server exposes 36 read only tools and works on every plan, including free. There is also an open source version you can run locally.

More than prices. Forex for more than 4,000 pairs, stock splits, company profiles, logos, and sector and industry reference data, plus bulk downloads on paid plans. Official clients exist for Python, JavaScript, Ruby and Go.

Pricing and limits

	Free	Individual	Professional	Enterprise
Price per month	0	29 dollars	89 dollars	Custom
Requests per minute	5	300	Unlimited	Unlimited
Price and financial history	2 years	5 years	40 plus years	40 plus years
Quarterly financials	None	20 quarters	160 plus quarters	160 plus quarters
Earnings transcripts	2 quarters	2 quarters	All available	All available
Earnings calendar and bulk downloads	No	No	Yes	Yes
MCP server access	Yes	Yes	Yes	Yes
Commercial license	No	No	No	Yes

Annual billing includes two free months. Free and Individual both cap transcripts at two quarters, so the 17 plus year transcript archive needs Professional.

The honest limits

It does not fit every job. Prices are daily only, so there is no intraday or tick data. There is no options or crypto data. And if you plan to redistribute the data inside a product, you need Enterprise, because the lower plans carry no commercial license. If any of those are your main need, look at the providers further down.

A first request

Authentication takes one header. Every v3 path starts with the version, and tickers use an exchange prefix such as NASDAQ:AAPL.

import os
import time
import requests

BASE = "https://api.roic.ai/v3.0.0"
HEADERS = {"Authorization": f"Bearer {os.environ['ROIC_API_KEY']}"}

def get(path, **params):
    r = requests.get(f"{BASE}{path}", headers=HEADERS, params=params, timeout=30)
    if r.status_code == 429:
        time.sleep(int(r.headers.get("Retry-After", 30)))
        return get(path, **params)
    r.raise_for_status()
    return r.json()

latest = get("/stock-prices/latest/NASDAQ:AAPL")
income = get(
    "/fundamental/income-statement/NASDAQ:AAPL",
    period_type="annual",
    **{"fiscal_year.gte": 2025},
)
print(latest)
print(income)

Two details from the docs are worth knowing. Send either the query parameter key or the bearer header, never both, because supplying both returns a 401. And a request counts against your limit before validation runs, so even a malformed call costs you one.

Connecting an AI assistant

The hosted MCP server lives at https://mcp.roic.ai/mcp. In Claude Code, one command adds it:

claude mcp add --transport http roicai https://mcp.roic.ai/mcp

For CI jobs or clients without hosted login support, there is also an open source server you can run locally with an API key. Review its license and recent commits on GitHub before adopting it, as you would with any third party package.

Once connected, you can ask for things like profitability ratios for two companies over three years, and the assistant routes the request to the right tool. Plan limits apply exactly as they do over REST.

For every endpoint, error code and the full MCP tool list, ROIC.ai has its own detailed guide:

TheBitForge for Roic AI

Oct 24

ROIC.ai Complete Guide - Financial Data API, Pricing, Endpoints, and the 36-Tool MCP Server

#ai #api #opensource #programming

13 min read

You can start free at roic.ai/signup with no credit card, and the interactive docs live at roic.ai/api/docs.

How to judge a finance data API

Most comparison posts rank by feature count. That is the wrong lens. A longer endpoint list does not help if the one endpoint you need is locked behind a plan you cannot afford. These are the questions I would ask, in this order.

What kind of data do I need? Prices, fundamentals, filings, news and transcripts are different products. Many providers are strong at one and thin at the rest.

Where are my companies listed? Several providers cover US stocks well and treat everything else as an upgrade.

How much history do I need? Free and entry plans often cap history at two to five years. That is fine for a dashboard and useless for a long backtest.

What will my call budget look like? Do this math before you pick a plan, not after.

calls per day = N_{symbols} \times R_{refreshes} \times E_{endpoints}

Say you track 500 tickers, refresh twice a day, and hit three endpoints for each. That is 3,000 calls a day. On a free tier capped at 25 requests per day, that job would take 120 days to finish once. And some vendors weight calls differently. EODHD, for example, charges 1 call for an end of day request but 10 for fundamentals, so the same job costs far more than the raw count suggests.

Am I allowed to ship this data? Personal use and commercial redistribution are different licenses. This is the one people skip, and it is the one that can end a product.

Can an AI agent use it? More providers now run MCP servers so assistants can query data directly. If you build with agents, that is worth checking.

The comparison at a glance

Provider	Best for	Free tier	Paid entry point	Standout limit
ROIC.ai	Fundamentals, ratios, transcripts	5 requests per minute, 2 years, MCP included	29 dollars a month for 300 per minute	Daily prices only
Alpha Vantage	Learning, scheduled jobs	25 requests per day, 5 per minute	49.99 dollars a month for 75 per minute	Free tier is tiny
Financial Modeling Prep	Statements and screening	250 calls per day, end of day	Starter, US only	Global data sits in the top plan
Massive (Polygon.io)	Live US trading data	5 calls per minute, 2 years end of day	29 dollars a month, 15 minute delay	US focus
Twelve Data	Multi asset time series	800 credits per day	29 dollars a month	Credit weights vary by endpoint
Finnhub	Fast prototypes	60 calls per minute, personal use	Priced per market	Free tier is non commercial
EODHD	Global history	20 calls per day	19.99 dollars a month	Fundamentals cost 10 calls each
Tiingo	Adjusted history	Free Starter plan	30 dollars a month	Fundamentals sold separately
SEC EDGAR	Filings and XBRL	Free, no key	Not applicable	10 requests per second, you normalize the data

The other best finance data APIs

Alpha Vantage

Alpha Vantage is where a lot of developers start, and there is a good reason. It has a huge tutorial base, a broad endpoint list that includes technical indicators, and a key you can get in seconds.

The free tier is the catch. You get 25 requests per day and 5 per minute, and the vendor's own community has noted that the daily number used to be 500, then 100, and is now 25. Paid plans run from 49.99 dollars a month for 75 requests per minute up to 249.99 for 1,200 per minute, and paid plans have no daily cap.

Use it for learning and for small scheduled jobs. Do not build anything that refreshes often on the free key.

import os
import requests

url = "https://www.alphavantage.co/query"
params = {
    "function": "GLOBAL_QUOTE",
    "symbol": "IBM",
    "apikey": os.environ["ALPHAVANTAGE_KEY"],
}
print(requests.get(url, params=params, timeout=30).json())

Financial Modeling Prep

Financial Modeling Prep, usually called FMP, leans toward statements, ratios and screening. The free Basic plan gives 250 calls per day with end of day data and a sample of tickers.

The paid ladder matters more than the free tier. Starter allows 300 calls per minute but covers US companies only. Premium raises that to 750 per minute and adds UK and Canadian coverage plus up to 30 years of history. Global coverage, earnings transcripts and 13F filings sit in Ultimate at 3,000 calls per minute.

One detail most reviews miss: FMP also enforces a trailing 30 day bandwidth cap, 500 MB on free, 20 GB on Starter, 50 GB on Premium and 150 GB on Ultimate. If you pull bulk statements, you can hit that before you hit the call limit.

Starter pricing shows up in the twenties on annual billing and a little higher month to month, so check the live page.

Massive, formerly Polygon.io

Polygon.io became Massive on October 30, 2025. The two API hostnames ran in parallel during the transition, and the plan prices I found match what Polygon charged under its old name.

Massive is the pick when you care about live US market data. It covers stocks, options, indices, forex and crypto, with WebSocket streaming and flat file downloads. Individual stock plans run from a free Basic tier (5 calls per minute, two years of end of day data) to Starter at 29 dollars a month, Developer at 79 and Advanced at 199. Starter data is delayed 15 minutes, so true real time costs more.

The tradeoff is scope. It is excellent at market data and less central for fundamentals, and it is US focused.

Twelve Data

Twelve Data offers one consistent interface across equities, ETFs, forex, crypto and indices, with server side technical indicators and WebSockets. The free plan gives 800 API credits per day, and paid plans start at 29 dollars a month.

The word to watch is credits. Different endpoints consume different amounts, so a naive call count will mislead you. Read their data weight explanation before you size a plan.

Finnhub

Finnhub has one of the most generous free tiers in the category: about 60 calls per minute, real time US quotes, company news, basic fundamentals, forex and crypto, and a WebSocket stream, all without a credit card.

The catch is the license. The free key is meant for personal, non commercial projects. Paid plans are priced per market and not as simple to read as a flat table, so expect to spend some time on the pricing page.

It is a great prototyping key. Confirm the terms before it goes near a product.

import os
import requests

r = requests.get(
    "https://finnhub.io/api/v1/quote",
    params={"symbol": "AAPL", "token": os.environ["FINNHUB_KEY"]},
    timeout=30,
)
print(r.json())

EODHD

EODHD, short for EOD Historical Data, is the long history specialist. The free plan gives only 20 calls per day, but the paid End of Day plan is 19.99 dollars a month and the End of Day plus Intraday plan is 29.99. Paid plans allow 100,000 calls per day and 1,000 per minute.

Remember the call weights. One call for end of day or live data, five for intraday and news, ten for fundamentals, and 100 or more for bulk. Commercial use goes through a separate startup and enterprise plan, so do not assume a 29.99 dollar plan covers a product.

Tiingo

Tiingo is the one I would reach for when the quality of adjusted price history and corporate actions matters most, such as backtesting. Its free Starter plan is limited to about 50 requests per hour and 500 unique symbols per month. The Power plan for individuals is 30 dollars a month, or 300 on annual billing, and a Business plan for internal company use is priced higher.

Fundamentals and news have historically sat behind paid access or add ons, so check what your plan includes before you plan around them.

SEC EDGAR

EDGAR is not a vendor. It is the primary source, and it is free. The SEC exposes company submissions and extracted XBRL facts through JSON endpoints on data.sec.gov, with no API key.

The rules are strict. The fair access guidance caps you at 10 requests per second across all your machines, and you must send a User-Agent that identifies you with contact details. Skip that header and you get a 403 quickly, and repeat offenders can be blocked.

You also do the normalizing yourself. Facts can appear under several taxonomies, and companies tag things differently. That is the price of free, as filed data, and it is the work a service like ROIC.ai does for you.

import time
import requests

HEADERS = {"User-Agent": "Your Name your.email@example.com"}

facts = requests.get(
    "https://data.sec.gov/api/xbrl/companyfacts/CIK0000320193.json",
    headers=HEADERS,
    timeout=30,
).json()

revenues = facts["facts"]["us-gaap"].get("Revenues", {})
print(list(revenues.get("units", {}).keys()))
time.sleep(0.2)  # stay far below 10 requests per second

A note on yfinance. It is a popular open source Python wrapper around Yahoo Finance data. It is great in a notebook, but it is not an official API with an agreement behind it, so I would not build a paid product on it.

Measure them yourself

Documentation tells you what a vendor promises. A ten minute test tells you what you will actually get from your own server. Here is a small harness that times a list of endpoints and prints the median and the slowest runs.

import os
import statistics
import time
import requests

def bench(name, url, headers=None, params=None, runs=10):
    times = []
    for _ in range(runs):
        start = time.perf_counter()
        r = requests.get(url, headers=headers, params=params, timeout=30)
        r.raise_for_status()
        times.append((time.perf_counter() - start) * 1000)
        time.sleep(1.5)  # respect free tier limits
    times.sort()
    print(f"{name}: median {statistics.median(times):.0f} ms, slowest {times[-1]:.0f} ms")

bench(
    "roic.ai",
    "https://api.roic.ai/v3.0.0/stock-prices/latest/NASDAQ:AAPL",
    headers={"Authorization": f"Bearer {os.environ['ROIC_API_KEY']}"},
)
# Add one line per provider, with your own keys

Run it from the region where your app will live. A fast result from your laptop in one country can look very different from a cloud server in another.

Mistakes that cost real money

Using unadjusted prices for returns. After a split or dividend, raw closes lie. Check whether an endpoint returns adjusted or unadjusted data before you compute anything.

Treating the free tier as a license. A key that works is not permission to redistribute. Read the terms for commercial use every time.

Counting calls without weights. The 3,000 calls you planned can turn into 30,000 on a provider that weights fundamentals.

Putting keys in query strings. URLs get logged by servers, proxies and browsers. Prefer an authorization header where the provider supports one.

Skipping the cache. Fundamentals change a few times a year. Fetch them once, store them, and stop paying for the same answer twice.

Ignoring symbol formats. Some providers want a bare ticker, others want an exchange prefix. A mismatch looks like missing data when it is really a wrong identifier.

Frequently asked questions

What is the best finance data API?
For company fundamentals, ratios and earnings transcripts across many markets, ROIC.ai is the one I would start with. For live US trading data it is Massive, and for a free prototype key it is Finnhub. The right answer depends on the data you need, so use the quick answer table near the top.

What is the best free finance data API?
For raw call volume, Finnhub's 60 calls per minute is the most generous, though it is for personal use. For fundamentals on a free key, ROIC.ai offers worldwide coverage, 5 requests per minute and MCP access, with two years of history. Alpha Vantage's 25 requests per day is the tightest of the group.

Which finance data API works best with AI agents?
Look for a native MCP server. ROIC.ai runs a hosted one with 36 read only tools on every plan, including free, and an open source version for local use. Several other providers have community built servers. Plan limits still apply to every tool call.

Which finance data API has real time stock data?
Massive, Finnhub and Twelve Data all offer real time or streaming options, with limits that depend on the plan. Several entry plans deliver data delayed by 15 minutes, so check the delay before you build anything that trades. ROIC.ai serves daily prices only.

Is there an official free source for company financials?
Yes. SEC EDGAR provides filings and XBRL facts for free with no key, within a limit of 10 requests per second and a required User-Agent header. You normalize the data yourself.

Can I use these APIs in a commercial product?
Often only on higher tiers. ROIC.ai reserves commercial redistribution for Enterprise, Finnhub's free key is non commercial, and EODHD routes commercial use to a separate plan. Always read the license, not just the pricing table.

Final thoughts

Choose by the job, not by the brand. For live US trading data, Massive is the natural fit. For a free prototype key, Finnhub and Twelve Data are hard to beat. For decades of global end of day history, look at EODHD. For clean adjusted prices, Tiingo. For official filings at no cost, go to EDGAR directly.

And for fundamentals, ratios and transcripts across many markets, with an MCP server for your AI tools and a free plan to test on, ROIC.ai is the one I would open first. Create a free key at roic.ai/signup, run the first request from this post, and see whether the data matches what you need.

Whatever you pick, run the call budget math first, read the license second, and test with your own script third. That order saves you from the expensive kind of surprise.

What are you building, and which API ended up in your stack? Tell me in the comments.

ROIC.ai Complete Guide - Financial Data API, Pricing, Endpoints, and the 36-Tool MCP Server

TheBitForge — Sat, 24 Oct 2026 06:13:05 +0000

ROIC.ai is a financial data API covering stock prices, financial statements, earnings call transcripts, financial ratios, and company data across 68,000+ tickers and 70+ global exchanges. This guide documents the full product: authentication, response formats, every endpoint category, exact pricing across all four tiers, and the complete 36-tool MCP server with setup steps for every supported client.

Overview
Data Coverage
Account Setup and API Keys
Authentication
Base URL and First Request
Response Format
Pagination
Error Codes
Rate Limits
Full Endpoint Reference
Pricing — All Four Plans
MCP Server
MCP Tool Reference (36 Tools)
MCP Setup by Client
Self-Hosting the MCP Server
SDKs
Known Limitations
Reference Links

Overview

ROIC.ai ( @vladcx ) operates as a REST API (api.roic.ai) with a parallel MCP server (mcp.roic.ai) that exposes the same data to AI assistants as callable tools. Both interfaces draw from the same underlying dataset and the same per-plan limits.

Published scale figures:

Metric	Value
Financial statement history	40+ years
Tickers covered	68,000+
Quarters of data per company	120+
Earnings call transcript history	17+ years
Global exchanges	70+
Advertised uptime	99.9%
Advertised response time	Sub-200ms

Commercial redistribution rights (shipping the data inside a product you sell) are available only on the Enterprise plan.

Data Coverage

The API is organized into four data groups.

Fundamentals

Income statement (annual + quarterly revenue, net income, EPS, EBITDA)
Balance sheet (assets, liabilities, equity, debt, cash positions)
Cash flow statement (operating, investing, financing, CapEx, free cash flow)
Pre-computed financial ratios and valuation metrics

Earnings

Earnings calendar (scheduled + reported, filterable by ticker/date)
Consensus estimates vs. reported actuals
EPS surprise history (beat/miss with calculated surprise size)
Full call transcripts — raw and edited versions, organized by fiscal quarter
Call audio and slide decks (where available)
SEC filings tied to earnings events

Market Data

Historical daily OHLCV and VWAP, adjusted or unadjusted for splits/dividends
Latest daily price (single ticker or market-wide snapshot)
Stock splits (forward and reverse, with ratios and execution dates)
Forex rates for 4,000+ currency pairs, historical and latest

Company Data

Company profiles (identity, classification, market cap, CEO, exchange listings)
Company logos (WebP or PNG, sized on request)
Sector and industry classification with ticker counts per category
SEC-sourced fiscal-year employee headcount history

Account Setup and API Keys

Go to roic.ai/signup
Create an account — no credit card required for the Free plan
Open your dashboard and copy your API key
The same key authenticates both API v2 and v3 requests

Authentication

Every request uses exactly one of two authentication methods. Supplying both on the same request returns 401 Unauthorized, even when both values are the correct key.

Method 1: Query parameter

curl "https://api.roic.ai/v3.0.0/company/profile/NASDAQ:AAPL?apikey=YOUR_API_KEY"

Suitable for quick testing in a browser or terminal. Not recommended for production, since query parameters are commonly logged by servers, proxies, and browser history.

Method 2: Bearer token (recommended)

curl "https://api.roic.ai/v3.0.0/company/profile/NASDAQ:AAPL" \
  -H "Authorization: Bearer $ROIC_API_KEY"

This is the documented method for production applications, server-side code, and scripts.

Key storage rules per the docs: store the key in an environment variable or secrets manager; never commit it to source control; never expose it in client-side browser code.

Base URL and First Request

https://api.roic.ai

Every v3 endpoint path begins with /v3.0.0. Example — company profile for Apple:

curl "https://api.roic.ai/v3.0.0/company/profile/NASDAQ:AAPL?apikey=YOUR_API_KEY"

A successful call returns 200 OK, a Request-Id response header (used for support/debugging), and the resource object directly.

Response Format

Single-resource responses

Returned as an unwrapped object — not nested under a data key:

{
  "id": "tkr_cO46h8pgSu9Qqy",
  "object": "v3.reference.ticker",
  "symbol": "NASDAQ:AAPL",
  "ticker": "AAPL"
}

Nullable fields are present with a null value when no data is available, rather than being omitted.

Exception: stock prices, forex prices, and forex pairs do not include id or object fields. A per-row ID would duplicate data already present in the request, so these resources are addressed by symbol and date instead:

{
  "symbol": "NASDAQ:AAPL",
  "date": "2026-07-17",
  "close": 212.41
}

List responses

Returned as a data array with pagination URLs:

{
  "data": [],
  "next_page_url": "/v3.0.0/tickers?limit=50&page=page_v1_opaque_token",
  "previous_page_url": null
}

First page: previous_page_url is null
Last page: next_page_url is null
Price-history list endpoints put the shared symbol and currency once on the envelope rather than repeating them on every row; these fields remain present even when data is empty

Binary response (one exception)

GET /v3.0.0/company/logo/{identifier} returns raw WebP or PNG bytes instead of JSON, with Content-Type, ETag, and Cache-Control headers. Conditional requests can return 304 Not Modified with an empty body. Logo failures still return the standard JSON error envelope.

Pagination

Most list endpoints use cursor-based pagination.

Request the first page without a page parameter:

curl "https://api.roic.ai/v3.0.0/tickers?limit=50" \
  -H "Authorization: Bearer $ROIC_API_KEY"

Follow the returned next_page_url:

curl "https://api.roic.ai/v3.0.0/tickers?limit=50&page=page_v1_opaque_token" \
  -H "Authorization: Bearer $ROIC_API_KEY"

Rules:

Page tokens are opaque — never decode, edit, or construct one manually. Each token is bound to the specific endpoint, filters, sort order, and limit that generated it.
Tokens expire after one hour.
Changing a filter, sort order, or page size requires starting a new request without a page parameter.
Query-authenticated clients must re-append ?apikey= on every followed page URL — returned page URLs never carry credentials.
The API uses stable keyset ordering, but underlying data can change between requests; store results locally if a reproducible dataset is required.
A small number of endpoints (e.g., the metric-definition list) are unpaginated — they return every matching record in one response and don't accept limit, page, or order; both page URLs are always null on these.

Error Codes

All errors use one consistent envelope:

{
  "error": {
    "type": "invalid_request_error",
    "code": "resource_missing",
    "message": "The requested resource does not exist.",
    "request_id": "req_0af7651916cd43dd8448eb211c80319c"
  }
}

Application logic should branch on type and code — not the human-readable message, which is not guaranteed to stay stable in wording. The optional param field, when present, identifies which request value needs to change.

HTTP Status	`type`	Meaning
400	`invalid_request_error`	Invalid parameter, body, or page token
401	`authentication_error`	Missing, conflicting, malformed, or unknown credentials
402	`payment_required_error`	Current plan does not include access to this resource
404	`invalid_request_error`	Resource or v3 route does not exist
409	`conflict_error`	Request conflicts with existing state
429	`rate_limit_error`	Request limit exceeded
500	`api_error`	Unexpected server-side failure
503	`api_error`	A required dependency is unavailable

Error responses carry Cache-Control: no-store and must not be cached. Every response — success or failure — includes a Request-Id header.

Documented retry policy:

429 → wait for the duration in Retry-After
500 / 503 → capped exponential backoff with jitter
Validation or authentication errors → correct the request before retrying; retrying unmodified requests will not succeed

Rate Limits

v2 and v3 requests draw from the same quota per API key, enforced on a sliding 60-second window. An authenticated request is counted against the quota before parameter validation runs, so even an invalid request consumes one call if the limit has not been reached.

Plan	Requests/minute	History available
Free	5	2 years
Individual	300	5 years
Professional	Unlimited	All available (40+ years)
Enterprise	Unlimited	All available (40+ years)

Rate-limit response headers

Present on most metered responses, but not guaranteed on every call:

Header	Meaning
`X-RateLimit-Limit`	Enforced limit for the current window
`X-RateLimit-Remaining`	Requests remaining after this one
`X-RateLimit-Reset`	Unix timestamp when the oldest request exits the window

429 response example

{
  "error": {
    "type": "rate_limit_error",
    "code": "rate_limit_exceeded",
    "message": "Rate limit exceeded for the free plan. Retry in 42 seconds or update your plan.",
    "request_id": "req_0af7651916cd43dd8448eb211c80319c"
  }
}

Full Endpoint Reference

Tickers

Endpoint	Purpose
List all tickers	Paginated catalog of all tickers
Retrieve a ticker	Single ticker resource
Search tickers	Resolve company name/symbol to ticker resources

Company

Endpoint	Purpose
Company profile	CEO, sector, industry, market cap, exchange listings, description
Company employees	Fiscal-year headcount history (SEC-sourced)
Company logo	Binary WebP/PNG, supports conditional requests

Earnings Calls

Endpoint	Purpose
List earnings calls	All available calls by year/quarter/date
Raw transcript	Unedited call transcript
Edited transcript	Cleaned/formatted transcript
Call audio	Audio file for the call
Slide decks	Presentation materials from the call
Filings	SEC filings tied to the earnings event

Calendar

Endpoint	Purpose
Earnings calendar	Scheduled and reported earnings with estimates/actuals/surprises

Stock Prices

Endpoint	Purpose
Historical prices	Daily OHLCV with date-range filtering
Latest price	Most recent quote, single ticker
List all latest stock prices	Market-wide latest-price snapshot

Stock Splits

Endpoint	Purpose
List stock splits	Market-wide or per-ticker split events

Financial Statements

Endpoint	Purpose
Income statement	Revenue, net income, EPS, EBITDA
Balance sheet	Assets, liabilities, equity, debt
Cash flow statement	Operating/investing/financing cash flow, CapEx, FCF

Financial Ratios

Endpoint	Purpose
Profitability ratios	ROE, ROA, ROIC, margins
Credit & debt ratios	Debt/EBITDA, debt/equity, interest coverage
Liquidity ratios	Current, quick, cash ratio, Altman Z-Score
Working capital ratios	Turnover ratios, cash conversion cycle
Yield analysis	FCF yield, shareholder yield, capital yield

Valuation

Endpoint	Purpose
Enterprise value	Market cap, EV, EV/Sales, EV/EBITDA
Valuation multiples	P/E, P/B, P/S, P/CF, EV multiples
Per-share data	EPS, revenue, book value, cash flow per share

Forex

Endpoint	Purpose
List forex pairs	Currency-pair catalog
Historical forex prices	Daily OHLC history for one pair
Latest forex price	Latest rate for one pair
List all latest forex prices	Latest rate, every pair

Bulk Downloads (paid plans only)

Endpoint	Purpose
Bulk fundamentals	Mass financial-statement export
Bulk earnings calls	Mass transcript export
Bulk stock prices	Mass price-history export

Reference Data

Endpoint	Purpose
Exchanges	All exchanges with ticker counts
Exchange trading hours	Full trading-session history per exchange
Exchange holidays	Holiday calendar per exchange
Markets	Markets with country codes and symbol counts
Sectors	All sectors with ticker counts
Industries	All industries with ticker counts
Countries	All countries with ticker counts

Full interactive documentation and the OpenAPI spec: roic.ai/api/docs

Pricing — All Four Plans

Billed monthly or annually; annual billing includes 2 free months.

Feature	Free	Individual	Professional	Enterprise
Price/month	$0	$29	$89	Custom
Requests/minute	5	300	Unlimited	Unlimited
Historical data	2 years	5 years	All available (40+ yrs)	All available (40+ yrs)
Market coverage	Worldwide	Worldwide	Worldwide	Worldwide
Annual financials	2 years	5 years	40+ years	40+ years
Quarterly financials	No	20 quarters	160+ quarters	160+ quarters
Financial ratios	2 years	5 years	40+ years	40+ years
Valuation metrics	2 years	5 years	40+ years	40+ years
Stock prices	2 years	5 years	All available	All available
Batch stock prices	No	Yes	Yes	Yes
Stock splits	2 years	5 years	All available	All available
Forex	2 years	5 years	All available	All available
Earnings call transcripts	2 quarters	2 quarters	All available	All available
Earnings calendar	No	No	Yes	Yes
Bulk downloads	No	No	Yes	Yes
Exchange calendars	No	Yes	Yes	Yes
MCP server access	Yes	Yes	Yes	Yes
Commercial-use license	No	No	No	Yes
Invoicing / multi-user access	No	No	No	Yes
Dedicated support	No	No	No	Yes

All four tiers include company profile data, ticker search, and reference data (sectors, industries, countries, exchanges) at no additional cost.

Note on earnings transcripts: both Free and Individual are capped at 2 quarters of call transcripts. Full transcript history (17+ years) is only unlocked on Professional and Enterprise, alongside the 40-year financial history.

MCP Server

ROIC.ai runs a hosted MCP (Model Context Protocol) server exposing 36 read-only tools to AI assistants. An assistant connected to the server selects the appropriate tool(s) based on a natural-language request, subject to the connected account's plan limits.

Server URL:

https://mcp.roic.ai/mcp

MCP server access is included on every plan, including Free. Plan limits (requests/minute, history depth) apply identically whether data is requested through the REST API or through MCP tool calls. Some calendar, exchange-schedule, and bulk-feed tools require a paid plan regardless of access method.

Plan	Requests/minute	History via MCP
Free	5	2 years
Individual	300	5 years
Professional	Unlimited	All available
Enterprise	Unlimited	All available

MCP Tool Reference (36 Tools)

Tickers & Discovery

Tool	Description
`search_tickers`	Search by ticker symbol or company name
`list_tickers`	List tickers with listing status, filterable by `listed`
`search_ticker_by_identifier`	Look up a ticker by CIK, CUSIP, or ISIN

Tool	Description
`get_company_profile`	CEO, employees, sector, industry, description
`list_employee_counts`	Historical fiscal-year employee counts
`get_company_news`	Recent news articles, paginated and date-filterable

Earnings Calls and Calendar

Tool	Description
`list_earnings_calendar`	Scheduled/reported earnings with estimates, actuals, surprises
`get_latest_earnings_call`	Most recent call transcript
`list_earnings_calls`	List all available calls by year, quarter, date
`get_earnings_call_transcript`	Specific transcript by year and quarter

Stock Prices and Splits

Tool	Description
`get_stock_prices`	Historical OHLCV with date-range filtering
`get_latest_stock_price`	Most recent price for one ticker
`list_latest_stock_prices`	Latest quote for every ticker, filterable by exchange
`list_stock_splits`	Market-wide or per-ticker split events

Financial Statements

Tool	Description
`get_income_statement`	Revenue, gross profit, operating income, net income, EPS, EBITDA, margins
`get_balance_sheet`	Assets, liabilities, equity, shares outstanding, debt
`get_cash_flow`	Operating/investing/financing cash flows, CapEx, free cash flow

Financial Ratios

Tool	Description
`get_profitability_ratios`	ROE, ROA, ROIC, margins, effective tax rate, payout ratio
`get_credit_ratios`	Debt/EBITDA, debt/equity, interest coverage
`get_liquidity_ratios`	Current ratio, quick ratio, cash ratio, Altman Z-Score
`get_working_capital_ratios`	Receivable/inventory/payable turnover, cash conversion cycle
`get_yield_analysis`	FCF yield, shareholder yield, capital yield

Valuation & Metrics

Tool	Description
`get_enterprise_value`	Market cap, enterprise value, EV/Sales, EV/EBITDA
`get_valuation_multiples`	P/E, P/B, P/S, P/CF, EV multiples (last/avg/high/low)
`get_per_share_data`	EPS, revenue, EBITDA, book value, cash flow per share

Reference Data

Tool	Description
`list_exchanges`	All exchanges with ticker counts
`get_exchange_trading_hours`	Full trading-session history for one exchange
`get_exchange_holidays`	Holiday calendar for one exchange
`list_markets`	Markets with country codes, exchanges, symbol counts
`list_sectors`	All sectors with ticker counts
`list_industries`	All industries with ticker counts
`list_countries`	All countries with ticker counts

Forex

Tool	Description
`list_forex_pairs`	Currency-pair catalog with base, quote, synthetic status
`get_forex_prices`	Historical daily OHLC rates, one pair
`get_latest_forex_price`	Latest daily rate, one pair
`list_latest_forex_prices`	Latest rate for every pair, filterable by currency

Example natural-language queries

Tell me about Apple's business. What do they do, who's the CEO,
and how many employees do they have?

Routes to get_company_profile.

Compare Microsoft and Google's profitability over the last 3 years.
Show me their margins and ROE.

Routes to get_profitability_ratios, called once per ticker.

What did Apple's management say about AI in their latest earnings call?

Routes to get_latest_earnings_call.

Is Tesla overvalued? Show me its P/E ratio, EV/EBITDA, and
price-to-sales compared to the last 5 years.

Routes to get_valuation_multiples with a historical range.

Show me Amazon's stock price performance for January 2025.

Routes to get_stock_prices with date filters.

Which sectors have the most publicly traded companies?

Routes to list_sectors.

MCP Setup by Client

ChatGPT

Open Settings → Apps → Advanced settings
Toggle Developer mode on
Click Create to open the New App configuration window
Fill in: Name Roic AI, Connection type Server URL, URL https://mcp.roic.ai/mcp
Check "I understand and want to continue", click Create
Click Sign in with Roic AI and authorize in the browser tab that opens
In a new chat, click + → More → Roic AI to attach the tool

Claude Code (CLI)

claude mcp add --transport http roicai https://mcp.roic.ai/mcp

Verify the connection:

claude mcp list

Claude (desktop/web)

Profile icon (bottom-left) → Customize
Connectors → Add custom connector
Name: Roic AI; Remote MCP server URL: https://mcp.roic.ai/mcp
Click Add, wait for the connection check, then click Connect
Authorize in the browser window that opens
Review Tool permissions (default may be "Needs approval"; can be changed to "Always allow")
Start a new chat and confirm the connector is active via the + icon

VS Code

Open Command Palette (Cmd+Shift+P / Ctrl+Shift+P)
Search for and select MCP: Add Server
Choose HTTP as the transport type
Enter URL: https://mcp.roic.ai/mcp
Name the server (e.g., roicai) and press Enter
Authorize in the browser window that opens

OpenAI Codex

CLI:

codex mcp add roicai --url https://mcp.roic.ai/mcp

Or in ~/.codex/config.toml:

[mcp_servers.roicai]
url = "https://mcp.roic.ai/mcp"

Manual JSON configuration (any compatible MCP client)

{
  "mcpServers": {
    "roicai": {
      "url": "https://mcp.roic.ai/mcp",
      "transport": "http"
    }
  }
}

Self-Hosting the MCP Server

The MCP server is open source, published at github.com/roicai/mcp-server. It runs locally with one command:

ROIC_API_KEY=your_key npx @roicai/mcp-server

This runs over stdio and authenticates with a standard REST API key instead of OAuth — relevant for CI pipelines, automation scripts, or any MCP client that doesn't support hosted OAuth flows.

Configuration for Claude Desktop, Cursor, or any command-based MCP client:

{
  "mcpServers": {
    "roicai": {
      "command": "npx",
      "args": ["-y", "@roicai/mcp-server"],
      "env": { "ROIC_API_KEY": "your_key" }
    }
  }
}

Review the repository's license and recent commit history directly on GitHub before adopting it as a dependency, as with any third-party package.

SDKs

Official SDKs/clients: Python, JavaScript/Node.js, Ruby, Go, plus raw cURL examples throughout the docs.

Python install and basic usage:

pip install roicai

from roicai import Roicai

client = Roicai(api_key="YOUR_API_KEY")
prices = client.stock_prices.list("NASDAQ:AAPL")

for day in prices.items:
    print(day.date, day.close)

Known Limitations

Based on the publicly available documentation at the time of writing, the API does not currently include:

Intraday or tick-level price data — historical and latest prices are daily OHLCV only
Options chain or derivatives data
Cryptocurrency data
Full earnings call transcript history on Free or Individual plans — both are capped at 2 quarters; full history requires Professional or Enterprise
Earnings calendar or bulk downloads on Free or Individual plans — both require Professional or above
Commercial redistribution rights outside the Enterprise plan

Reference Links

Resource	Link
Homepage	roic.ai
Sign up	roic.ai/signup
API docs (full)	roic.ai/api/docs
Getting started	roic.ai/api/docs/getting-started
Authentication docs	roic.ai/api/docs/authentication
Response format docs	roic.ai/api/docs/response-format
OpenAPI spec	roic.ai/api/docs/openapi
MCP docs	roic.ai/api/mcp
Self-hosted MCP (GitHub)	github.com/roicai/mcp-server
Pricing	roic.ai/pricing
Status page	status.roic.ai
Base API URL	`https://api.roic.ai`
MCP server URL	`https://mcp.roic.ai/mcp`

All pricing, rate limits, and endpoint details in this guide reflect the public documentation at the time of writing. APIs and pricing change over time — verify current details at roic.ai/pricing and roic.ai/api/docs before making integration decisions based on this reference.

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ROIC AI empowers developers, investors, and analysts with financial data APIs, company fundamentals, valuation metrics, Python tutorials, and AI-powered research to build smarter applications and uncover actionable insights.

101 Best Financial Data APIs in 2026

TheBitForge — Fri, 23 Oct 2026 11:18:31 +0000

101 Best Financial Data APIs in 2026 - DEV Community

101 financial data APIs for stocks, fundamentals, filings, crypto, forex, macro, news and banking. What each does best, plus how to choose one.

dev.to

101 Best Financial Data APIs in 2026

TheBitForge — Fri, 23 Oct 2026 11:17:44 +0000

You find the perfect side project idea, open the docs of the first finance API Google gives you, and twenty minutes later you're staring at a rate-limit error, a pricing page full of "Contact sales" buttons, or data that quietly stops at 2019.

So we went through a long list of financial data APIs, dropped the ones that shut down, got renamed or were never real APIs, and sorted the rest by what you're actually building. Each section below is a table you can scan in seconds.

TL;DR

If you need...	Start with
Clean fundamentals and ready-made ratios	roic.ai, Financial Modeling Prep
A free key to prototype with	Alpha Vantage, Finnhub, Twelve Data
Tick-level and options data	Massive (ex-Polygon.io), Databento
SEC filings	SEC EDGAR, SEC-API.io
Global macro data	FRED, World Bank, Trading Economics
Crypto prices	CoinGecko, CoinMarketCap
Currency conversion	Open Exchange Rates, Frankfurter
Bank account data	Plaid, TrueLayer
Enterprise-grade coverage	Bloomberg, LSEG, FactSet, S&P Capital IQ

What's inside

Featured Pick: A Fundamentals-First Financial Data API (1-1)
Stock Market Data and Fundamentals APIs (2-28)
SEC Filings, Company and Research APIs (29-36)
Government and Central Bank Data APIs (37-48)
Crypto Data APIs (49-62)
Forex and Currency APIs (63-72)
Fixed Income, Derivatives and Exchange Data (73-79)
News, Sentiment and Alternative Data APIs (80-88)
Banking, Open Banking and Payments APIs (89-101)
Platforms we left out
FAQ

How to pick one without regret

Question	Why it matters
What data do you actually need?	Prices, fundamentals, filings, macro, crypto and FX each have different leaders
How fresh must it be?	End-of-day is cheap and everywhere, real-time and tick data cost real money
Where do your tickers trade?	Many free APIs are great for US stocks and thin everywhere else
What do the licensing terms say?	Public display or redistribution often needs a different plan than internal use
What if the provider vanishes?	IEX Cloud shut down on August 31, 2024, so keep your integration layer thin
What are the rate limits?	A free tier that throttles at 5 calls a minute is a demo, not a product

💡 Tip: test with the ten tickers you really care about, including awkward ones like recent IPOs, ADRs and delisted names. It tells you more than any comparison table.

Featured Pick: A Fundamentals-First Financial Data API

If you're building anything that depends on clean company financials, start here.

1. roic.ai ( @vladcx )

The write-up below sticks to what its public docs say.

roic.ai is a fundamentals-first API. Instead of handing you raw statements and leaving the math to you, it returns standardized financial statements next to pre-calculated ratios like ROIC, ROE, debt-to-EBITDA and Altman Z-score, plus valuation multiples and per-share data.

What stands out:

Wide coverage: public companies across 70+ global exchanges.
Flexible identifiers: API v2 accepts ticker, CIK, CUSIP and ISIN, so you skip a lot of symbol mapping.
Earnings call transcripts: a dedicated transcripts API sits beside statements and ratios.
AI-ready: an official MCP server connects Claude, ChatGPT, Cursor, VS Code and Codex to the data, with OAuth sign-in or an API key.
Spreadsheet friendly: a Google Sheets integration pulls the same fundamentals into your sheets.
Easy start: the docs describe a free tier with no credit card, and paid plans raise limits and history depth.

Here's the whole "hello world" from their getting-started docs:

curl "https://api.roic.ai/v2/company/profile/AAPL?apikey=YOUR_API_KEY"

Best for: fundamental investors, equity research tools, AI assistants that need reliable numbers, and devs who'd rather not compute ratios themselves.

Heads-up: it's fundamentals-first. If you need tick-level data or order books, pair it with something like Databento or Massive. Check the pricing page for current limits.

Explore the roic.ai API →

👉 Get a free roic.ai API key → and run it on a few tickers you already know.

Stock Market Data and Fundamentals APIs

This is the busiest corner of the market, and the one most developers care about first. Prices, financial statements, ratios, filings, and technical indicators all live here.

#	Provider	What it is	Good to know
2	Financial Modeling Prep (FMP)	FMP is a go-to for statements, ratios, and market prices in one REST API.	It also covers ETFs, forex, crypto, and news. Developers like that the endpoints are predictable and the free tier lets you test before paying. It also marketed itself as an alternative after IEX Cloud closed.
3	Alpha Vantage	One of the most recognizable names for hobbyists and startups.	You get stocks, ETFs, FX, crypto, commodities, fundamentals, and a long list of built-in technical indicators over simple JSON or CSV calls. It has a free key, and it has added MCP support for AI agents. The free tier is rate-limited, so plan for that.
4	EOD Historical Data (EODHD)	EODHD focuses on broad global coverage at a lower price than the enterprise vendors.	It offers end-of-day, intraday, fundamentals, options, and forex data across a large number of exchanges. A solid choice when you need international tickers without an enterprise contract.
5	Massive (formerly Polygon.io)	Polygon.io rebranded as Massive on October 30, 2025.	The old api.polygon.io base is still supported, with a parallel api.massive.com base. It is best known for deep tick-level and options data across stocks, options, forex, and crypto, and it is priced per asset class.
6	Databento	Databento delivers raw, normalized market data straight from exchange feeds, with historical and live access.	It suits quant teams, backtesting, and anyone who cares about tick-level accuracy and clean symbology more than a friendly spreadsheet export.
7	Tiingo	Tiingo pairs clean end-of-day price history with news and a developer-friendly API.	It has a long-standing reputation for data quality among retail quants and Python users, with a free tier for experimenting.
8	Finnhub	Finnhub covers real-time US quotes, fundamentals, company news, alternative datasets, and forex and crypto.	Its free tier is generous enough for side projects, which is why it shows up in so many tutorials.
9	Twelve Data	Twelve Data offers time series, technical indicators, fundamentals, and WebSocket streaming across stocks, forex, and crypto.	The documentation is tidy and the client libraries are easy to get running quickly.
10	Intrinio	Intrinio sells standardized fundamentals, real-time and historical prices, options, and other datasets with clear licensing options.	It was the provider IEX named when it told customers about its closure, and it is aimed at fintechs that need compliant redistribution.
11	FinancialData.net	A newer entrant covering stock prices, fundamentals, insider transactions, and more through a simple API.	Worth a look if you want one provider for several datasets and clear flat-rate pricing.
12	Marketstack	Part of the APILayer family, Marketstack provides real-time, intraday, and end-of-day stock data from many global exchanges.	It is simple, low-cost, and a comfortable pick for dashboards and basic apps.
13	Nasdaq Data Link (formerly Quandl)	The home of the datasets that made Quandl famous, now under Nasdaq.	You can browse a large catalog of free and premium datasets covering markets, economics, and alternative data, accessed through one consistent API.
14	Xignite	A cloud market-data provider used by banks, brokerages, and fintechs.	Morningstar acquired Xignite in 2019, so it sits in the same family as Morningstar's data products. Expect a sales-led process rather than a self-serve signup.
15	Barchart OnDemand	Barchart's APIs cover equities, futures, options, and forex, with a particularly strong reputation for futures and commodities data.	They are usually bought by firms that want licensed exchange data with support.
16	Tradier	Tradier is a brokerage with an API that exposes quotes, options chains, and trading.	It is a handy choice if you want market data and order execution from the same developer platform.
17	Alpaca Market Data	Alpaca is an API-first broker that also sells market data for US stocks, options, and crypto.	It is popular with algo traders who want data and paper trading in the same ecosystem.
18	Yahoo Finance (unofficial, via yfinance)	Yahoo no longer offers a supported public API, but the open-source yfinance library scrapes its pages and is everywhere in tutorials.	Great for learning and quick experiments. We would not build a paid product on it, because there is no SLA and it can break without notice.
19	Morningstar	Morningstar's developer portal sells fund, equity, ESG, and ratings data.	If your product revolves around mutual funds and ETFs, Morningstar's research depth is hard to beat, though it comes with enterprise pricing.
20	FactSet	FactSet offers a wide set of APIs for fundamentals, estimates, ownership, and market data, mostly used by institutions.	Expect enterprise contracts and strong support.
21	S&P Capital IQ	S&P Global Market Intelligence provides company financials, estimates, and credit data through Capital IQ products and APIs.	It is an institutional standard for corporate research.
22	LSEG Data & Analytics (formerly Refinitiv)	Refinitiv is now part of the London Stock Exchange Group.	The developer portal exposes real-time and historical data, fundamentals, news, and Datastream content. It is built for large firms with licensing needs.
23	Bloomberg (B-PIPE and Data License)	The heavyweight of the industry.	Bloomberg offers real-time feeds and reference data for firms that already live in its ecosystem. It is rarely the right call for a small team, but it is the benchmark everyone else is measured against.
24	ICE Data Services	ICE sells exchange data, reference data, and pricing for equities, fixed income, and derivatives.	It is part of the company that owns the New York Stock Exchange.
25	Zacks	Zacks is known for earnings estimates, rankings, and stock research.	It has data products for institutions and developers who want its estimate-driven view of stocks.
26	TradeStation API	TradeStation exposes market data and trading through its brokerage API, covering equities, options, and futures.	It is a practical option for traders building automated strategies.
27	Charles Schwab Trader API	Schwab's developer portal offers market data and trading APIs for clients, including quotes and order management.	Access requires an approved developer app and a brokerage relationship, so budget time for onboarding.
28	Interactive Brokers API	Interactive Brokers provides APIs for market data and order execution across global markets, but you need a brokerage account.	It is powerful and a bit old-school to set up.

SEC Filings, Company and Research APIs

Filings and research data are where fundamental investors and AI builders spend a lot of time. Some of the best sources here cost nothing.

#	Provider	What it is	Good to know
29	SEC EDGAR APIs	The SEC's own APIs deliver filing metadata and structured XBRL data for US public companies, free of charge.	Raw EDGAR is the ground truth for 10-Ks and 8-Ks, though you will do some parsing work yourself. Respect the SEC's fair-access limits.
30	SEC-API.io	A commercial layer on top of EDGAR that offers search, real-time filing streams, and parsed sections of filings.	It saves a lot of engineering time if you need filings turned into usable JSON.
31	AlphaSense	AlphaSense is a market-intelligence platform for filings, transcripts, broker research, and news, with enterprise APIs.	It absorbed Sentieo, the older research platform some lists still mention.
32	Crunchbase	Crunchbase's API covers private companies, funding rounds, investors, and acquisitions.	It is the usual starting point for startup and venture data.
33	CB Insights	CB Insights offers tech and startup intelligence, deals, and market maps, with enterprise data access.	It suits corporate strategy and VC teams more than individual developers.
34	MSCI	MSCI is a major provider of indexes, risk analytics, and ESG ratings.	Its data is typically licensed by asset managers and large platforms.
35	RepRisk	RepRisk specializes in ESG and business-conduct risk data built from media and public sources.	It is a common choice for compliance and sustainable-investing workflows.
36	Trading Economics	Trading Economics serves macro indicators, forecasts, and market data for a large number of countries.	It is a convenient single source when you need global economic series in one place.

Government and Central Bank Data APIs

These are official sources. They are free, authoritative, and often more stable than commercial vendors, although the formats can be quirky.

#	Provider	What it is	Good to know
37	FRED (St. Louis Fed)	FRED hosts hundreds of thousands of economic time series, from interest rates to inflation, through a free API key.	It is the first stop for anyone building an economics dashboard.
38	World Bank Open Data API	Free, well-documented access to development indicators for most of the world's economies.	Good for long-term, cross-country analysis.
39	IMF Data	The International Monetary Fund publishes international financial statistics and macro data through its data portal and web services.	It's useful for balance-of-payments, exchange rates, and country-level economics.
40	OECD Data API	The OECD provides statistics for its member economies through an SDMX-based API.	It is excellent for comparing developed economies on labor, trade, and prices.
41	BIS Data Portal	The Bank for International Settlements shares banking, credit, and exchange-rate statistics, including data via its bulk download and API tools.	It is niche but valuable for systemic-risk research.
42	ECB Data Portal	The European Central Bank's data portal exposes euro-area statistics and official euro foreign exchange reference rates.	It replaced the older Statistical Data Warehouse interface.
43	Eurostat API	Eurostat offers EU-wide economic and social statistics through web services.	Handy for anything tied to European macro trends.
44	Bank of England Database	The Bank of England publishes UK interest rates, gilt yields, and monetary statistics in its Statistical Interactive Database, with CSV and XML access.	—
45	Bank of Canada Valet API	Valet is the Bank of Canada's free API for exchange rates, interest rates, and other time series.	It is simple and refreshingly easy to use.
46	Bank of Japan Time-Series Data	The Bank of Japan's statistics portal provides time-series data on rates, money supply, and prices.	Check the current documentation for programmatic access options.
47	Banque de France Webstat	Banque de France publishes French and euro-area statistics through Webstat, including an API for series access.	—
48	U.S. Treasury FiscalData API	The US Treasury's free API covers debt, interest rates, exchange rates, and federal finance data.	No signup required for most endpoints.

Crypto Data APIs

Crypto data is its own universe, with thousands of assets, hundreds of exchanges, and lots of ways for the numbers to disagree.

#	Provider	What it is	Good to know
49	CoinGecko API	CoinGecko describes itself as an independent crypto data aggregator, and its API covers prices, market caps, exchange data, NFTs, and DeFi metrics for a huge range of coins.	A free tier exists, with paid plans for higher limits.
50	CoinMarketCap API	CoinMarketCap offers rankings, quotes, and metadata for crypto assets and exchanges.	It's the other big name next to CoinGecko, with a free basic plan.
51	CoinDesk Data (formerly CryptoCompare)	CryptoCompare's data business now operates under the CoinDesk Data brand.	It provides real-time and historical prices, order books, and indices aimed at both developers and institutions.
52	Messari	Messari combines market data with research, asset profiles, and on-chain fundamentals.	A good fit for analysts who want context along with prices.
53	Kaiko	Kaiko provides institutional-grade tick data, order books, and reference rates across many exchanges.	It is targeted at funds, banks, and index providers.
54	CoinAPI	CoinAPI normalizes trades, quotes, and order books from a large number of crypto exchanges into one API, with REST, WebSocket, and FIX access.	—
55	CoinCap API	CoinCap offers simple, real-time crypto prices and market data via REST and WebSocket.	A friendly pick for demos and lightweight apps.
56	Glassnode	Glassnode is known for on-chain analytics such as active addresses, supply metrics, and market indicators.	Its API is popular with analysts who trade on fundamentals of the chain itself.
57	Coinbase Exchange and Advanced Trade API	Coinbase exposes market data and trading endpoints for its exchange.	Useful when you only need prices and execution for assets listed there.
58	Kraken API	Kraken provides public market data and authenticated trading through REST and WebSocket APIs.	Public endpoints work without an account.
59	Binance API	Binance's public APIs cover spot and derivatives market data with very high liquidity coverage.	Availability and rules vary by region, so check where your users are.
60	Gemini API	Gemini offers market data and trading APIs for its US-regulated exchange, with public endpoints for prices and order books.	—
61	BitMEX API	BitMEX provides market data and trading for crypto derivatives like perpetual swaps.	It suits derivatives-focused traders and researchers.
62	Bitstamp API	One of the longest-running exchanges, Bitstamp provides a clean public API for spot prices and order books.	—

Forex and Currency APIs

Currency conversion looks easy until you need history, reliability, and a sensible rate limit. These APIs cover the spectrum from free to broker-grade.

#	Provider	What it is	Good to know
63	Open Exchange Rates	A long-running JSON API for exchange rates with a free plan and paid tiers for higher volume, more frequent updates, and more features.	It is widely used in e-commerce and finance apps.
64	Fixer	Another APILayer product, Fixer returns live and historical rates for a wide set of currencies, with a free plan to start.	—
65	Currencylayer	Also from APILayer, Currencylayer provides real-time and historical rates for many currencies with a free tier and paid upgrades.	—
66	XE Currency Data	XE is a household name in currency conversion, and its data API serves commercial users.	Pricing is paid.
67	OANDA Exchange Rates API	OANDA offers exchange-rate data and, for account holders, trading APIs.	It is a regulated broker with a long history in FX.
68	1Forge	1Forge focuses on real-time forex quotes with a free tier and paid plans.	Check current availability and plans before committing, as smaller vendors change often.
69	TrueFX	TrueFX provides free tick-level interbank forex data after registration, which is handy for backtesting.	—
70	ExchangeRate-API	A straightforward currency API with a free plan and simple pricing.	Very quick to integrate.
71	Frankfurter	A free, open-source exchange-rate API built on reference rates published by central banks, mainly the European Central Bank.	No key required.
72	Currencycloud	Currencycloud, now part of Visa, offers cross-border payments and FX APIs.	It suits fintechs that need to move money internationally.

Fixed Income, Derivatives and Exchange Data

Bond and derivatives data tends to be more fragmented and more expensive. These are the exchanges, utilities, and platforms behind many feeds.

#	Provider	What it is	Good to know
73	CME Group Market Data	CME Group offers real-time and historical futures and options data, including its DataMine historical archive.	It is the primary source for CME-listed products.
74	Cboe Market Data	Cboe provides options, equities, and volatility index data through its market data services.	The go-to source for VIX-related products.
75	MarketAxess	MarketAxess runs an electronic bond-trading platform and provides credit market data and analytics to subscribers.	—
76	Tradeweb	Tradeweb operates electronic markets for rates, credit, and derivatives, and provides market data to clients.	—
77	DTCC Data	DTCC runs core clearing and settlement infrastructure and publishes data products, including public trade repository reports.	—
78	FINRA TRACE	FINRA's TRACE system collects US corporate bond trade reports and distributes the data through licensed feeds and public statistics.	—
79	MSRB EMMA	EMMA is the official source for US municipal bond disclosures and trade data, offered by the Municipal Securities Rulemaking Board.	—

News, Sentiment and Alternative Data APIs

Headlines and sentiment are noisy by nature. The providers below range from free community sources to quant-grade feeds.

#	Provider	What it is	Good to know
80	NewsAPI.org	A simple API for headlines and articles from many publishers.	Good for prototypes, with limitations on free use.
81	Webz.io (formerly Webhose)	Webz.io delivers structured news, blog, and forum data from across the open and dark web, aimed at monitoring and analytics teams.	—
82	Aylien (now part of Quantexa)	Aylien built news-intelligence and NLP APIs for entity extraction and sentiment, and is now part of Quantexa.	Confirm current product availability before building on it.
83	StockTwits API	StockTwits offers social sentiment from a community of retail traders.	Useful as a signal, as long as you remember it reflects crowd mood rather than facts.
84	GDELT Project	GDELT monitors global news and publishes free datasets and APIs for events, tone, and themes.	A favorite of researchers.
85	RavenPack	RavenPack turns news and text into structured analytics and sentiment for quant funds and risk teams.	Enterprise pricing applies.
86	Meltwater	Meltwater provides media monitoring and social listening with API access, mostly for marketing and communications teams.	—
87	Dow Jones Factiva	Factiva is a deep archive of global news and business content with enterprise APIs.	Built for corporate and institutional research.
88	Benzinga APIs	Benzinga provides real-time news, calendars, and market-moving headlines to traders and fintech apps.	—

Banking, Open Banking and Payments APIs

Not exactly market data, but if your product touches users' money, you will meet these names quickly.

#	Provider	What it is	Good to know
89	Plaid	Plaid connects apps to users' bank accounts for balances, transactions, identity, and payments.	It is the most recognized account-aggregation API in North America and also operates in parts of Europe.
90	TrueLayer	TrueLayer offers open banking data and payment initiation across the UK and Europe.	It is a staple for European fintechs.
91	Yodlee	Yodlee, from Envestnet, has long provided account aggregation and financial data enrichment at scale.	—
92	Tink	Tink is a European open banking platform, now owned by Visa, covering account data, payments, and money-management tools.	—
93	Finicity	Finicity, part of Mastercard, provides account verification and financial data for lending and personal finance use cases in the US.	—
94	MX	MX provides data aggregation and enrichment to banks and fintechs, with a focus on cleaner transaction data.	—
95	Stripe	Stripe is primarily a payments platform, but its APIs for payments, billing, and financial connections make it part of many fintech stacks.	—
96	Square Developer	Square's APIs power payments, payouts, and commerce for businesses of all sizes.	—
97	Dwolla	Dwolla offers an API for ACH bank-to-bank payments in the US, aimed at platforms that move money between accounts.	—
98	GoCardless Bank Account Data	GoCardless provides open banking account data across Europe and the UK through a developer-friendly API.	—
99	Wise Platform	Wise Platform lets businesses embed international transfers and multi-currency accounts through its APIs.	A practical option when your product needs cross-border payouts with transparent FX.
100	BBVA API Market	BBVA's API marketplace exposes banking services such as accounts and payments to partners.	—
101	Open Banking UK	Not a data vendor itself, but the standards body behind UK open banking.	Its directory and specifications are essential reading if you build there.

Platforms we left out (and why)

Name	Why it's not here
IEX Cloud	Retired all products on August 31, 2024
Polygon.io (old name)	Still alive, now called Massive, so it's listed that way
Nomics	We couldn't confirm it still serves a public API
Google Finance	No official public API
Sentieo	Folded into AlphaSense, which has its own entry
Venmo / Cash App	No public financial-data APIs
Made-up providers	If a vendor can't be found on the real web, it doesn't belong here

Before you commit

Do this	Because
Compare two vendors	Small differences in standardizing fundamentals can change a ratio
Read the terms	Personal use, internal use and public display are priced differently
Cache what you can	Lower bill and fewer outages
Check the live pricing page	Plans change, and no guide can replace the real page

FAQ

What's the best free financial data API?
For prototypes, Alpha Vantage, Finnhub and Twelve Data are common starting points, and roic.ai documents a free tier with no credit card. Free plans are rate-limited, so check the limits against your use case.

Which API is best for fundamentals?
Look for standardized statements and pre-computed ratios. roic.ai, Financial Modeling Prep, Intrinio and FactSet are all built around fundamentals, at very different price points.

What's the best API for real-time stock data?
Massive (formerly Polygon.io), Databento, Alpaca and Finnhub are popular for real-time US data. Confirm whether you're getting consolidated, exchange-licensed or delayed data.

Is there an official Yahoo Finance API?
No. The popular yfinance library scrapes Yahoo pages, which is fine for learning but risky in production.

Where can I get SEC filings through an API?
SEC EDGAR has free APIs. SEC-API.io and AlphaSense add search and parsing on top.

Which API is best for crypto data?
CoinGecko and CoinMarketCap cover broad market data, Kaiko and CoinAPI suit institutional needs, and Glassnode specializes in on-chain metrics.

Can AI assistants use financial data APIs?
Yes. Several providers now offer Model Context Protocol (MCP) servers so tools like Claude, ChatGPT and Cursor can query data directly. roic.ai and Alpha Vantage both document MCP support.

Can I make investment decisions from this data?
A data API only delivers data. It isn't advice, and data can be wrong or delayed. Verify important figures against primary sources.

Where we'd start

There's no single winner, because "best" depends on what you're building. Prototype with a free key, use official sources for macro data, and pay for exchange-grade feeds only when your product truly needs them.

If your project is built on company fundamentals, we'd begin with roic.ai. It returns standardized statements, ready-made ratios, valuation multiples and earnings transcripts through one REST API, and plugs into AI assistants through MCP.

👉 Get a free roic.ai API key →

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Are We Becoming Better Developers, or Just Faster at Producing Code?

TheBitForge — Sat, 17 Oct 2026 10:43:36 +0000

Friday afternoon. A ticket lands in your queue: new endpoint, some validation, a small migration. The kind of task that used to eat half your day.

You describe it to your AI assistant, skim the diff, run the tests, ship it. Twenty minutes, done.

Three months later that endpoint breaks under some load pattern nobody planned for. You open the file and you don't quite recognize it. You wrote it, technically. You accepted it, tested it, merged it. But you never built a real model of how it works, because the part that would have forced you to think it through got handled for you.

That raises a question: if AI writes more of our code, are we still learning how that code actually works, or just getting faster at shipping things we don't fully understand?

The productivity studies don't agree, and the disagreement is the useful part

In 2023, Microsoft, GitHub, and MIT Sloan ran a randomized trial where developers wrote a standardized HTTP server in JavaScript. The Copilot group finished 55.8% faster, and more of them finished at all, 78% versus 70%, though that completion-rate gap wasn't statistically significant on its own. It measured speed on a fairly contained task, not whether the resulting code held up over time.

Two years later, METR tested something closer to real engineering work. They recruited sixteen experienced open source developers and watched them complete 246 tasks in their own large, familiar repositories, some with AI access and some without. The result ran the other direction: developers using AI took 19% longer. Before starting, they'd predicted AI would speed them up. Afterward, they still believed it had, estimating a roughly 20% improvement that the data didn't support.

At first glance these look contradictory. But the studies tested different kinds of work. A contained task with an unfamiliar tool is where AI assistance tends to show up well. A codebase someone already carries in their head, built over years, is a much harder place for a generic tool to add value, and an easier place for review and correction overhead to eat whatever time it saves. The studies tell us something different about productivity in different settings. METR's result is the one worth sitting with: developers expected AI to save them time, and the measured result went the other way.

The quiz that tested what developers actually learned

A more direct test of the real question came from Anthropic, in a study of 52 developers, mostly junior, learning an unfamiliar Python library called Trio. One group had an AI assistant during the coding tasks. The other coded by hand. Afterward, everyone took the same quiz, no AI allowed, covering debugging, code reading, and conceptual understanding. It's a small study of people learning something new, not a representative sample of senior engineers working on production systems, but the results are worth examining.

The AI-assisted group averaged 50% on that quiz, versus 67% for the hand-coding group, a gap of 17 percentage points. The biggest difference between the groups showed up specifically on debugging questions. There's another important detail, though: the AI group only finished about two minutes faster on average, a difference too small to call statistically meaningful. They didn't even get much speed in exchange for the gap in understanding.

Looking back at screen recordings, the researchers sorted participants into six observational patterns of how people actually used the assistant. Three patterns, built around handing the task over and accepting what came back, averaged under 40% on the quiz. Three other patterns, where people asked why something worked or requested explanations alongside the code, scored between 65% and 86%. The researchers are careful that this is a correlation found after the fact, not proof that a given pattern of use caused the score. Still, the association is hard to ignore: people who stayed cognitively engaged with the assistant tended to retain more than people who didn't.

What shows up later, in actual codebases

GitClear analyzed roughly 153 million lines of changed code from 2020 through 2023 and projected that code churn, meaning lines reverted or rewritten within two weeks of being written, would roughly double by 2024 compared with its 2021 baseline. Copy-pasted code was also rising relative to properly refactored code. GitClear shows that churn increased during the years AI tools became common, but that doesn't prove AI caused it, and GitHub has pushed back publicly on that reading. One possible explanation is that developers are merging code before they've fully understood it, though the data alone can't establish that.

On security specifically, Stanford researchers found that developers with access to an AI coding assistant wrote measurably less secure code than a control group, and were more confident their code was secure. The tools have changed since that 2022 to 2023 experiment, so its results shouldn't be treated as a verdict on today's assistants. But it highlights a risk worth considering: developers can be confident in code they haven't fully evaluated.

The 2025 Stack Overflow survey, built on more than 49,000 responses, found 84% of developers either using AI tools already or planning to, a figure that mixes current users with people who haven't started yet. Among respondents overall, 46% said they didn't trust the accuracy of AI output, up from 31% the year before, and 66% named "almost right, but not quite" answers as their top frustration with the tools. Of those who answered the frustrations question, 45% said debugging AI-generated code was more time-consuming than expected. That's self-reported experience, not a controlled measurement of actual debugging time, but it lines up with the other findings here.

Where the tool genuinely helps

The Copilot study's speed gains were real, and some people in the Anthropic study using the exact same tool on the exact same task learned just as much as the control group. AI tends to help most with the parts of the job that are mechanical once you already understand them: boilerplate, syntax you've half forgotten, turning a plan you've already worked out into code, explaining an unfamiliar error faster than a search engine would. It's also useful as a second opinion, something to push back against your own design before committing to it. None of this says AI damages learning by default. It says learning depends on what a developer does while the tool is open, not just whether it's open.

Rate limiting: two ways to use AI

Picture two developers given the same task: add rate limiting to an API with an AI assistant available to both. This is a made-up scenario to keep the point concrete, not a documented incident.

The first developer asks for an implementation, gets a working solution using a sliding-window counter backed by Redis, confirms the tests pass, and ships it. Weeks later, under a burst of traffic, the Redis connection pool starts timing out. They're trying to understand the retry logic and connection-pool behavior while the service is degraded, without knowing whether the problem is in their implementation, their Redis configuration, or both.

The second developer asks the same initial question, then follows up. Why this approach over an alternative. What happens if Redis briefly drops. They try implementing a small piece by hand first, compare it against what the assistant produced, and only merge once they can explain why the retry logic is structured the way it is. The task takes longer. When the same Redis issue shows up later, they already have a working model of the failure, because they built one on purpose while it was still possible to.

Neither developer is permanently stuck this way. The first could still learn the retry logic properly before anything breaks, and plenty of developers do exactly that. What separates them isn't talent or diligence in some abstract sense. It's that one of them paid a small cost upfront, and the other is likely paying a larger one later, probably at a worse time.

A short list of things worth trying

Sketch your own approach before asking the assistant for one, even roughly. Ask for the reasoning behind a suggestion, not only the code. Review generated code like you're the one debugging it alone later, because you probably will be. Write tests that check actual behavior rather than confirming a demo ran once. When something breaks, spend a few minutes forming your own guess before asking the AI to fix it. Try explaining a piece of merged code out loud before you consider the task finished. The Anthropic findings offer a reason to take these habits seriously: the people who asked questions and worked to understand the code generally scored better than those who just let AI do the work.

The real variable

If AI saves you an hour, use some of that time to actually understand what it produced. If you just move to the next ticket, you might finish more work today without being any better prepared to debug it tomorrow.

Pick one piece of AI-assisted code you shipped this month. Could you walk a teammate through why it's built the way it is, without reopening the chat?

TheBitForgeFollow

Founder @thebitforge.dev • Software & AI Product Agency • 181K+ readers & 7K+ followers • We turn ideas into digital products people 💙 to use | visit us also on insta @thebitforge.dev

DEV Community Doesn't Feel Like the Same Community Anymore

TheBitForge — Fri, 16 Oct 2026 15:14:16 +0000

I joined DEV Community in 2024, and honestly, it used to feel like a genuinely good place for developers.

People published articles, readers discovered new writers, discussions happened, and even smaller creators had a chance to get noticed. It wasn't just about publishing content and hoping someone would find it. There was a real sense of community.

Now, I'm not sure what has changed, but something definitely feels different.

And I know I'm not the only person who feels this way.

1. DEV Support: Does It Actually Work?

Let's start with support.

What is the point of having a support system if users can send multiple emails and still receive no response?

I'm not talking about expecting an instant reply to every message. I'm talking about receiving no meaningful response at all, even when users are dealing with actual account or platform issues.

Support is supposed to help users resolve problems, not leave them wondering whether anyone has even read their messages.

At this point, I genuinely question how useful DEV's support system is when you need it most.

2. A Username Change That Turned Into a Profile Problem

Recently, I changed my username from thebitforge to thebitforge_dev, then changed it back to thebitforge.

Since then, neither profile URL has been working as expected.

Opening dev.to/thebitforge redirects me to dev.to/thebitforge_dev, and the URLs keep behaving inconsistently. I can't reliably access my profile through either address.

Maybe this is a routing issue, a username migration bug, or something else entirely. I don't know. That's precisely why support exists.

I've experienced other issues before, too, and getting a response has been a problem.

How are users supposed to resolve platform issues when the official support channel provides no meaningful help?

3. Where Has Article Discovery Gone?

This is another major concern.

I remember when publishing an article on DEV felt like an opportunity to start a conversation. People discovered posts, left comments, shared experiences, and engaged with writers they hadn't followed before.

Now, getting an article in front of readers feels much harder.

My articles don't seem to receive the same visibility they used to, and I've discussed this with other users who have expressed similar concerns about declining traffic and engagement.

Of course, traffic fluctuates. Algorithms change, competition increases, and not every article will perform well.

But when multiple writers start noticing the same pattern, I think it's reasonable to ask questions.

Have there been changes to article distribution? Has the recommendation system changed? Is the platform doing enough to help readers discover new writers?

I don't know the answers, and that's part of the problem. From a writer's perspective, it's difficult to understand what's happening.

A community where people can publish but struggle to reach readers risks losing the very discussions that make it valuable.

4. AI Moderation and New Account Suspensions

This is probably one of my biggest concerns.

On three occasions, I helped friends create DEV accounts. They signed up using Google, added their names, usernames, bios, tech stacks, and interests to complete their profiles, and their accounts were suspended within one or two seconds of finishing.

These were the experiences I personally witnessed. I cannot claim to know exactly what triggered the suspensions or whether an automated system was responsible.

But if legitimate new users can be suspended almost immediately while completing ordinary profile information, something deserves closer examination.

If automated moderation is involved, how reliably does it distinguish suspicious activity from normal onboarding?

And when an account is suspended, is there an accessible way to understand why and appeal the decision?

Automation can help platforms operate at scale. But automation without effective review and support can turn a simple signup into a frustrating experience.

5. What Happened to the Community Feel?

The DEV Community I remember from 2024 felt more active and interactive.

Writers had opportunities to get discovered. Readers participated in discussions. Publishing an article felt like joining a conversation rather than dropping a link into an endless feed.

I still believe DEV has good people and that its core idea is valuable. I'm not writing this because I want to see the platform fail.

I'm writing this because I remember when it felt better to participate.

I don't know which platform updates, ranking changes, or moderation decisions may have contributed to what I'm experiencing. Without transparent information, I won't pretend to know the cause.

But I do think users should be able to raise these concerns without their experiences being dismissed.

DEV, a few honest questions:

Why is getting meaningful support so difficult for users experiencing platform issues?
What happens when username changes leave profile URLs behaving inconsistently?
Have article discovery and distribution changed, and can writers get a clearer explanation of how their work reaches readers?
How can new users appeal suspensions that appear to happen almost immediately after signup?
What is being done to preserve the discussions and discoverability that make a developer community worth participating in?

I'm not claiming that DEV has become useless for everyone. I'm sharing what I've experienced and what I've heard from other writers I've spoken with.

But here's the uncomfortable question:

If developers cannot reliably get support, new users struggle to keep their accounts, and writers feel they are losing the opportunity to be discovered, what exactly is being done to maintain the community experience that made DEV valuable in the first place?

A community isn't just a publishing platform. It's the people, the conversations, the trust, and the opportunity to be heard.

I hope DEV hasn't forgotten that.

I'd like to bring these concerns to the attention of @francistrdev @peter @ben @jess @sloan @thepracticaldev @launches @noahbrinker @heyitsjem and the DEV Community team. I'd genuinely appreciate your response.

What I Look for Before Trusting an AI App Builder With a Real Project

TheBitForge — Fri, 16 Oct 2026 06:26:24 +0000

What I Look for Before Trusting an AI App Builder With a Real Project

AI app builders can now generate a working database, authentication flow, and deployed URL from a natural-language prompt. But a convincing demo is only the beginning: what happens when real users, permissions, and persistent data enter the picture?

I went looking for a concrete answer by digging into Floot, a YC-backed AI app builder that plugs into Claude, ChatGPT, and Cursor as an MCP connector, since it's one of the more recent entrants making a specific architectural claim: that bundling the whole stack (backend, database, hosting, auth) into one AI-native framework produces more reliable output than tools that bolt AI onto existing dev services.

I haven't built a production app with Floot myself, so I'm not going to pretend otherwise. What I can offer is a framework: the specific things I'd want answered, with sources, before handing any AI app builder, Floot or otherwise, a real project. If you've used Floot or a competitor for something beyond a prototype, I'd genuinely like to hear where this framework holds up and where it doesn't.

How Floot's workflow actually works

Worth establishing first, since the architecture is the whole pitch, and it's worth getting precise about which architecture, because Floot actually ships two.

The one most of the current marketing leads with is an MCP (Model Context Protocol) connector: instead of a separate web app, you add Floot to Claude, ChatGPT, Cursor, or another MCP client, and build from inside that chat. Floot's own documentation is specific about who does what here: your assistant creates the project, writes the files, provisions the database, runs the app, reads its own errors, and publishes, each as a direct tool call against your project. Floot's role in this path is the infrastructure underneath (a managed Postgres database, auth, file storage, hosting), not a second AI model reviewing the first one's work. Whatever model you're already paying for (Claude, GPT) is the one doing the interpreting and writing, but the development feedback loop involves more than the model reviewing its own output.

That's a meaningfully different claim than the one I originally saw repeated across older coverage of Floot, which describes a three-pass system: one AI interprets the request, a second writes the code, and a first or third reviews and fixes it. That description is real, but it traces back to Floot's original standalone product, the in-app editor with its own built-in AI agent, which Floot still offers as a separate path alongside the connector. If you're building through the MCP connector, which is the model this article is mostly about, that multi-pass AI review isn't the mechanism in play. However, Floot says it does run development checks in the background and feeds their results back to the assistant.

That distinction matters for how much scrutiny to apply. Even without a second AI model reviewing the first one's work, Floot says it type-checks code in the background after each batch of edits, checks backend code for frontend imports, and sends preview compile errors, build errors, and runtime logs back to the assistant. So the feedback loop isn't just the model catching its own mistakes; it also gets signals from actual development tools. That's an important distinction, although how reliably this catches deeper issues beyond compilation and runtime errors is still something I'd want to test on a real project.

The six things I'd actually test

This is the part I think is missing from most AI-builder coverage: a list of claims to verify, not a verdict. If you're evaluating Floot (or Bolt, Lovable, Replit, Base44) for something real, these are the specific things I'd check before trusting the output, not just the marketing page.

What to test	Why it matters
User isolation	A high-impact failure mode to test for: create two accounts and check whether either can access the other's data. It won't show up unless you actually try it with two test accounts; a single-account demo can't reveal it either way.
Auth edge cases	What happens when a logged-out user hits a page that should require login? Does the generated app enforce that server-side, or only hide the UI element client-side (which doesn't actually protect anything)?
Data persistence	Does data survive a refresh, a logout/login cycle, a browser restart? Sounds basic, but it's a real gap category in early-stage generated backends.
Error handling	What does the app do when required input is missing or malformed? Does it fail gracefully or expose something it shouldn't?
Deployment from a second machine	Can someone other than you open the published URL and complete the core workflow, cold?
Export, for real	Does the exported project actually build on its own, or does it fail on references to platform-managed services?

That last row is worth unpacking, because it's where the gap between "you can export" and "you have a runnable app" actually lives, and it's where Floot's own documentation is unusually candid for a product page.

Here's what's explicitly documented, directly: you can export both code (via a "Get Code" option, paid plans) and your database (a standard PostgreSQL dump, any plan). The docs also state that some built-in services get injected during Floot's build process and are not included in the export, and that any reference to them left in your exported code "will fail to resolve," which the docs themselves describe as a hard build break, not a minor inconvenience.

Here's what I'm inferring from that, rather than what's stated outright: that in practice, export isn't a one-click escape hatch so much as a migration task, since you'd need to replace those managed-service references before the exported project builds on its own. Floot's documentation does recommend doing that migration with your AI assistant's help before you export, which supports the inference, but the actual scope of that work (how many references, how much rewriting) will vary by project and isn't something the docs quantify.

That's a reasonable trade-off for the convenience of a managed stack. But exporting source code and running an application independently are two different milestones, and developers should evaluate them separately, a distinction that's easy to miss unless you read the docs past the pricing page.

Where this still comes down to judgment, not tooling

A few things I don't think any amount of reading documentation will resolve, and where I'd want direct experience before forming an opinion:

How good is the self-correction loop, really? The connector flow combines the assistant's own debugging with feedback from Floot's development tooling, including type checks, compile errors, build errors, and runtime logs. That's more than the model simply reviewing its own output. What I'd still want to understand is how reliably this catches deeper issues, particularly authorization mistakes, data isolation problems, and other bugs that don't necessarily produce an error during development. Whether this holds up across a representative range of app complexity, not just the CRUD-app demos that tend to get shown off, is an empirical question I'd want to investigate by testing a real project.
Security posture. I couldn't locate a publicly available, independent security audit of Floot's database or auth layer, which isn't proof one doesn't exist, just a gap in what's publicly findable. One G2 reviewer using the product said directly they weren't fully confident how secure Floot's databases were. That's a single anecdotal account, not evidence of an actual flaw, but it's a more candid data point than anything on the pricing page, and worth weighing accordingly, not dismissing or inflating.
Performance under real load. Hosting is included and scales with usage-based allowances rather than hard caps, per the pricing page, but I couldn't find a publicly available independent benchmark for how a Floot-built app behaves under sustained traffic.

None of this is a reason to write the category off. It's a reason to treat "the AI built it" as the start of your evaluation, not the end of it, the same way you'd treat a junior engineer's first pass at a feature: promising, worth reviewing, not worth shipping unexamined.

The actual question for discussion

If you've taken an AI app builder, Floot or otherwise, past the demo and into something with real users: what broke first? Was it a permissions bug, a performance ceiling, a debugging dead end where the AI couldn't fix what it had generated, or something else entirely? And for anyone who's exported a project off one of these platforms: how much rework did it actually take to get it running independently?

I'd rather build out the second half of this (the actual test matrix above, run against a real project) as a follow-up than speculate further without evidence. If that's useful to people here, let me know what you'd want tested first.

I'm planning to actually build something real on Floot soon and come back with the results, the six tests above, run for real, with whatever breaks along the way included. If that follow-up would be useful, say so in the comments and I'll prioritize it.

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How to Build Your First MCP Server: Step-by-Step Guide for Developers (2026)

TheBitForge — Fri, 02 Oct 2026 19:50:55 +0000

READ HERE:

How to Build Your First MCP Server: Step-by-Step Guide for Developers (2026) - TopBlogs

Build a working MCP server with the current 2026 SDKs, test it in the MCP Inspector, and connect it to Claude Code. Includes mistakes other developers hit.

topblogs.online

Last updated: October 2026

If you followed an MCP tutorial last year and the code no longer matches the official docs, your setup is probably fine. The protocol shipped its largest revision on July 28, 2026, and it changed how servers are written. A lot of older guides still teach the previous version.

This guide builds a small MCP server from scratch with the current SDKs. You will end up with two tools that you can test in the MCP Inspector and connect to Claude Code. If Node.js is already installed, the basic server can be running in roughly fifteen minutes. Allow longer if you are setting up the environment or Claude Code for the first time.

How this guide was put together: the implementation follows the current MCP documentation and SDK examples. The troubleshooting section also draws on reports from developers who have built MCP servers in practice. Those reports are credited where they appear and listed at the end.

What an MCP server actually does

An MCP server is a program that gives an AI application access to tools, and optionally to data and prompt templates, through one standard interface. The AI application is the client. It reads your list of tools and passes it to the model. When the model decides it needs one, the client sends a tools/call request, your server does the work, and the result goes back. The model never talks to your server directly.

Every tool has a name, a description, and an input schema. The model uses the description to decide whether your tool fits the task. A vague description leads to poor tool selection, so spend real time on it.

What changed in 2026, and why older tutorials break

The current protocol revision is 2026-07-28. Its headline change is a stateless protocol core. According to the official release announcement, the initialize handshake and the Mcp-Session-Id header were retired, and each request now carries its own protocol version and client details.

This affects you in two ways.

The SDK package layout changed. In the TypeScript SDK, v2 replaces the single @modelcontextprotocol/sdk package with separate packages, including @modelcontextprotocol/server and @modelcontextprotocol/client. In the Python SDK, v2 renamed FastMCP to MCPServer. If a tutorial imports from @modelcontextprotocol/sdk or mcp.server.fastmcp, it is using the older SDK API. Check which protocol revision the tutorial supports before following it.

State moves into your tools. A stateless protocol core does not force your application to be stateless. It means the modern protocol no longer relies on a transport-level session to carry state between requests. If your server must carry something across calls, the maintainers recommend returning an explicit handle from a tool, such as a job ID, and letting the model pass it back as an argument. The remote hosting section below shows an example.

What we are building

A server called blog-helper with two tools. reading-time estimates how long a text takes to read. make-slug turns a post title into a URL slug.

These are toy tools on purpose. They need no API keys and no network access, and they cover the two things every tool does: take structured input and return text. If something fails, the cause is in your MCP setup and not in a third-party service.

Step 1: Set up the project

You need Node.js 22.19 or later. The TypeScript SDK supports Node.js 20 and up, but the MCP Inspector used in Step 3 requires Node.js 22.19.0 or newer, so installing that version once covers the whole guide. The SDK ships ES modules only, so the project must be set to type: module. The tsx package runs TypeScript directly, so there is no build step.

mkdir blog-helper && cd blog-helper
npm init -y
npm pkg set type=module
npm install @modelcontextprotocol/server zod tsx
mkdir src

Step 2: Write the server

Create src/index.ts and paste in the following.

import { McpServer } from '@modelcontextprotocol/server';
import { serveStdio } from '@modelcontextprotocol/server/stdio';
import * as z from 'zod/v4';

function createServer(): McpServer {
    const server = new McpServer({ name: 'blog-helper', version: '1.0.0' });

    server.registerTool(
        'reading-time',
        {
            description:
                'Estimate how many minutes a piece of text takes to read. Use it for blog posts and articles.',
            inputSchema: z.object({
                text: z.string().min(1).describe('The full text to measure'),
                wordsPerMinute: z
                    .number()
                    .int()
                    .min(100)
                    .max(400)
                    .optional()
                    .describe('Reading speed. Defaults to 220.')
            })
        },
        async ({ text, wordsPerMinute }) => {
            const words = text.trim().split(/\s+/).length;
            const minutes = Math.max(1, Math.round(words / (wordsPerMinute ?? 220)));
            return {
                content: [{ type: 'text', text: `${words} words, about ${minutes} min read` }]
            };
        }
    );

    server.registerTool(
        'make-slug',
        {
            description: 'Turn a post title into a clean, lowercase URL slug.',
            inputSchema: z.object({
                title: z.string().min(1).max(200).describe('The post title')
            })
        },
        async ({ title }) => {
            const slug = title
                .toLowerCase()
                .normalize('NFKD')
                .replace(/[^\w\s-]/g, '')
                .trim()
                .replace(/[\s_-]+/g, '-');
            return { content: [{ type: 'text', text: slug }] };
        }
    );

    return server;
}

void serveStdio(createServer);
console.error('blog-helper MCP server running on stdio');

Three details deserve a closer look.

The Zod schema in inputSchema is the only schema you write. The SDK uses it to describe the tool's input to the client and to reject invalid arguments before your handler runs, which is why neither handler contains manual validation.

The server is built inside a createServer function because serveStdio calls it to create the instance that serves the connection. Keep that function lightweight: construct the server and register its tools there, and avoid slow network calls or expensive setup before the connection starts.

The last line logs with console.error on purpose. On a stdio server, stdout carries the protocol messages, so diagnostic output should go to stderr instead. A stray console.log writes non-protocol data to stdout and can corrupt the JSON-RPC stream. Several developers have reported running into this, and their accounts appear later in the article.

Step 3: Run it and test it in the Inspector

Start the server:

npx tsx src/index.ts

It prints the banner and then sits idle. That is expected, because a stdio server waits on stdin until a client starts talking to it.

To call the tools, use the MCP Inspector, a local web app that launches your command and connects to it over stdio.

npx @modelcontextprotocol/inspector npx tsx src/index.ts

The command prints a URL containing a one-time token for opening the Inspector's web interface. That is an access token for the Inspector, not the Mcp-Session-Id that older protocol revisions used. Open the URL in your browser, click Connect, open the Tools tab, choose make-slug, and enter a title. Then call reading-time with an empty string. The schema requires at least one character, so the call should be rejected before your handler runs. That rejection is a quick way to see MCP input validation in action.

For reference, the code above should produce results like these for valid input:

make-slug      "My First MCP Server in 2026"  ->  my-first-mcp-server-in-2026
reading-time   "Hello world"                 ->  2 words, about 1 min read

The developer behind the DEV Community post "How I Built My First MCP Server for Claude Code" (yureki_lab) wrote that they spent the first few hours debugging by asking Claude questions and judging whether the answers looked right. Moving to the Inspector gave them a direct view of what the server returned, with no model in between. It is worth testing the server on its own before connecting any client.

Step 4: Connect it to Claude Code

Registering a local stdio server takes one command. Everything after the double dash is the command Claude Code runs to launch your server. Use an absolute path to the entry file so it works from any folder.

claude mcp add blog-helper -- npx tsx /full/path/to/blog-helper/src/index.ts

Then run claude mcp list to confirm the server shows as connected. If it fails, check the path first. On Windows, write the path in the form your shell expects.

With the server connected, ask Claude for a slug, for example: "What would be a good URL slug for 'My First MCP Server in 2026'?" It should call your make-slug tool and use the result.

The same server in Python

If you work in Python, the v2 SDK builds a server from decorated functions. You need Python 3.10 or later. Set up the project with uv:

uv init blog-helper && cd blog-helper
uv add "mcp[cli]"

Then create server.py:

from mcp.server.mcpserver import MCPServer

mcp = MCPServer("blog-helper")


@mcp.tool()
def reading_time(text: str, words_per_minute: int = 220) -> str:
    """Estimate how many minutes a piece of text takes to read."""
    words = len(text.split())
    minutes = max(1, round(words / words_per_minute))
    return f"{words} words, about {minutes} min read"


if __name__ == "__main__":
    mcp.run(transport="stdio")

Type hints become the input schema, and the docstring becomes the tool description. Python v2 is a major rework of the SDK, so check its migration guide if an example here behaves differently on your version. pip install mcp now installs the 2.x line, so if you maintain older code and are not ready to migrate, keep a <2 upper bound on your requirement.

What other developers ran into

The official tutorials show the happy path. The reports below come from developers who built, tested, or shipped MCP servers, and each one is credited to its author.

Why stdout breaks stdio servers

Several developers have reported the same problem. yureki_lab, who built a server so Claude Code could query an internal service catalog, wrote that a single stray console.log cost about 40 minutes and produced a cryptic parse error. brianmello, whose server grew from a weekend project to roughly 2,300 npm downloads, called stdout the first surprise and added that the stray output can come from a dependency deep in your tree, not only from your own code. Both ended up sending every diagnostic message to stderr.

The official MCP debugging documentation gives the same rule for local servers and notes that the host application captures what you write to stderr. On Windows, Claude Desktop writes its logs under %APPDATA%\Claude\logs, which is a good first place to look when a connection fails.

Why relative paths cause confusing failures

Jaypee's troubleshooting guide to MCP connection errors points out that relative paths in a server config resolve against the client's working directory, not your server's. A server that works from your terminal can therefore fail inside Claude Desktop, and the fix is to use absolute paths.

The same guide lists a few other traps. A server can connect successfully yet expose zero tools if its input schemas are malformed. A server that needs an API key often fails at the first tool call and not at connect time. And a failed npx download looks like any other connection error, so run the server command by hand in a terminal before blaming the client.

Designing tools around user intent

The team behind thunderbit-mcp (author handle ethan_thunderbit) wrote that the first temptation is to wrap every endpoint of an existing API as a tool. Their field guide argues for fewer, more predictable tools, and it recommends adding negative guidance to descriptions, meaning a line that tells the model when not to use a tool. They also describe stdio as the calmest first transport, since users install the package and add a config entry.

brianmello gave related advice in one line: write tool descriptions like prompts, because the model reads them as prompts.

Making errors useful to agents

parkerrrrr observed that when a tool throws, the agent often sees only a generic internal error and cannot tell an authentication failure from a bad argument or an upstream outage. Catching errors in your handler and returning a clear message with isError: true lets the model see what went wrong and react sensibly.

Authentication on remote servers

The same author warned that an HTTP MCP server with authentication switched off is exactly the kind of default that gets found and abused. If you move beyond stdio, require a bearer token or OAuth, and make the server reject requests when no token is configured.

Migrating to the 2026-07-28 spec

krlz, writing on DEV Community about the new revision, expects the biggest breakage to come from elicitation, meaning a server asking the user a question mid-call. Because the back-channel is gone, code that relied on it must be rewritten around the new multi round-trip pattern.

The official release announcement includes comments from adopters. Supabase's head of product said the new pattern lets its tools confirm with the user before acting, for example before creating a project that costs money. Manufact reported that moving its mcp-use framework to the new SDK cut package size by about 83% and made it about 25% faster, thanks to the client and server split. Honeycomb said agents already make nearly 20% of its monthly interactive queries. These are vendor-reported figures, so treat them as signals about where the ecosystem is heading and not as independent benchmarks.

Going from local to remote

A stdio server runs on your own machine and is started by the app that uses it. To offer one endpoint that many people connect to, you serve the same server over HTTP instead.

The stateless protocol core makes this much easier. For clients that use the 2026-07-28 revision, requests no longer depend on an Mcp-Session-Id, so any request can land on any server instance behind an ordinary round-robin load balancer. Older protocol versions still have session behavior, so if your deployment also serves older clients, plan for that.

Say a tool starts a long export. Have it return a job ID, and add a second tool that accepts the ID and reports progress. The ID lives in the model's context, so it does not matter which machine answers the next call.

Security basics before you share anything

A stateless protocol core is not automatically a safe one. If your server touches files, databases, or accounts, build a few habits in from the start.

OWASP's MCP security guidance recommends running local servers in a sandbox such as a container, applying least privilege per server and per tool, and validating inputs and outputs at the server layer. Tool descriptions and tool outputs both become part of what the model reads, so treat them as untrusted input and not as instructions to follow. A model can be steered by text hidden inside either one.

If your server calls other APIs on a user's behalf, do not forward the token you received from the client. The MCP security best practices describe this "token passthrough" as an anti-pattern, and the authorization guidance says the server must not pass through the token it received. The upstream API should get a token issued for it, because a server that accepts tokens meant for another service can be abused.

Also ask for human confirmation before anything destructive, such as deleting data or moving money.

Quick answers

Does this only work with Claude?
No. MCP is an open standard, and clients such as VS Code and Cursor support it as well, so the same server can serve several tools.

TypeScript or Python for a first server?
Both have current v2 SDKs that support the 2026-07-28 specification, so the choice comes down to your stack. This guide's TypeScript path needs Node.js 22.19 or later, while the Python path needs Python 3.10 or later. The TypeScript SDK also publishes adapters for Express, Fastify, and Hono, which helps if you later want to host the server inside an existing web app.

How likely is the spec to break my server again?
The MCP project now documents a formal deprecation policy with a twelve-month minimum window for protocol changes, so removals come with a long notice period. SDK major versions, like the v1 to v2 jump, can still require code changes.

Where to go from here

Replace the two toy tools with something you actually need: a query against your own database, a search over your notes, or a check on your site's status.

For a first real implementation, keep the server read-only. That limits the damage while you learn how the model chooses and calls your tools, and you can add write access once its behavior looks predictable.

Sources

Official documentation and specification:

Model Context Protocol Blog, "The 2026-07-28 Specification" (David Soria Parra and Den Delimarsky, July 28, 2026), including the adopter comments from Supabase, Manufact, and Honeycomb
Model Context Protocol, specification 2026-07-28 and its versioning page
Model Context Protocol, "Security best practices" and authorization security considerations
Model Context Protocol, debugging documentation
Model Context Protocol, MCP Inspector documentation (Node.js 22.19.0 requirement)
Model Context Protocol TypeScript SDK, README and "Build your first server" tutorial (GitHub)
Model Context Protocol Python SDK, README and v1 to v2 migration guide
Claude Code documentation, "Connect Claude Code to tools via MCP"
OWASP, "MCP Security Cheat Sheet"

Developer experience reports (DEV Community):

"How I Built My First MCP Server for Claude Code: 5 Lessons Learned" (author handle: yureki_lab)
"Building an MCP server in production: lessons from 2,300 npm downloads" (author handle: brianmello)
"The parts of building an MCP server that the tutorials skip" (author handle: parkerrrrr)
"Building an MCP server: lessons from thunderbit-mcp" (author handle: ethan_thunderbit)
"Debugging MCP Server Connection Issues: 7 Common Errors and Fixes" (author: Jaypee)
"MCP Went Stateless: What the 2026-07-28 Spec Actually Changes" (author handle: krlz)

How to Do Keyword Research for Free: A Step-by-Step Guide for Beginners

TheBitForge — Tue, 29 Sep 2026 14:05:02 +0000

Most people pick blog topics the same way. They think of something they know about, write it up, hit publish, and wait. Then nothing happens, and they decide the writing must be the problem.

Sometimes it is. But a lot of the time, nobody was searching for that topic, or the people who were wanted something different from what the post gave them. Keyword research is just the habit of checking this before you spend a whole weekend on a draft.

You don't need a paid tool for it. Ahrefs and Semrush are good products, and if you ever outgrow the free options you'll know without being told. Until then, Google hands out most of what a beginner needs, and a few small tools fill the gaps.

Below is the order I'd use them in. One hypothetical example runs through every step: a small blog about making coffee at home, and a post on cold brew you're thinking of writing. Alongside it, I've pulled in real stories from other people and companies who did this kind of research, with the source named each time. Those are their results, told as they told them, so treat the numbers as their claims and not as a guarantee for your own site.

What you're actually looking for

A keyword is whatever someone types into a search box. Sometimes it's a single word. More often it's a messy half-question. "Cold brew" is a keyword. So is "how long does homemade cold brew last in the fridge."

When I look at a possible keyword, I ask myself three things. Are people searching for it? Could a site like mine realistically compete? And would the person searching be happy to land on the page I'd write?

Beginners skip that last one all the time. Someone typing "buy cold brew maker" wants a shop, not your 2,000-word essay on steeping time. Marcus Sheridan, the pool company owner who turned blogging into a business, makes the same point in his talks, as summarized by the Spiderworking blog. A post like "5 fun games to play in your swimming pool" might bring visitors, but they may have no plans to buy a pool. A search like "best pool providers in Richmond Virginia" comes from someone who is shopping. Same subject, very different reader.

Every keyword number you'll see in any tool is an estimate, even in the expensive ones. Use the numbers to compare one idea against another. Don't read them as a promise of traffic.

Step 1: Start with what's already in your head

Before you open any tool, make a plain list of 15 or 20 topics. Not keywords, just topics. What do readers ask you? What confused you when you were starting out? What did you google last month?

For the coffee blog, that list might be: cold brew ratios, best beans for cold brew, mason jar versus pitcher, why cold brew turns bitter, how long it keeps, concentrate versus ready-to-drink.

Then go and see how real people phrase these things. Search your topic on Reddit and skim the titles. Read YouTube comments under popular videos. Check Quora. You're hunting for wording, because people don't write "optimal steeping duration." They write "why is my cold brew so bitter??"

How this worked for River Pools. The best-known example of starting from customer questions is River Pools and Spas. After the 2008 crash hit his business, Marcus Sheridan sat down and listed every question buyers had ever asked him about fiberglass pools, then wrote an answer to each one. According to PRNEWS's coverage of his Content Marketing World keynote, the first article was "How Much Does a Fiberglass Pool Cost?", a question he says nobody in the industry was answering openly. PRNEWS reports that he credits that one article with $150,000 in sales, and that he tells other businesses to turn their top 100 customer questions into 100 posts. In a later podcast interview with Find Your Yellow Tux, he described writing those answers late at night, one at a time, and said the site eventually became the most trafficked swimming pool website in the world. Those figures are his own, so read them as his account. The method is the useful part: he started from real questions, not from a keyword tool.

Starting here matters more than it sounds. If you begin with a tool, you'll chase the same suggestions everyone else is chasing.

Step 2: Let Google finish your sentences

Open Google in a private window so your own search history doesn't bend the results. Type your seed topic and stop before you hit enter. The suggestions that drop down can show you phrases people commonly search for. They're not search volume data, but they're free ideas, and they're phrased the way real people talk.

Now play with it. Type "cold brew how" and see what appears. Then "cold brew why," "cold brew vs," "cold brew for." Try each letter of the alphabet after the seed, too. It's tedious and it works.

Next, search the phrase properly and look at two spots on the results page. The "People also ask" box is one. Click a question and it expands and often adds new ones below it. The "Related searches" list at the very bottom is the other.

You can automate a lot of this. AnswerThePublic and AlsoAsked both turn a seed keyword into a map of questions, and both have free usage that's capped, so check what you get before you plan around them.

Copy everything worth keeping into a spreadsheet. Don't judge anything yet.

Step 3: Get a rough sense of volume with Keyword Planner

Keyword Planner is Google's own tool, so it's one of the most direct free sources of Google's own keyword estimates. It lives inside Google Ads, which sounds like a trap. It isn't, as long as you're careful.

The usual route goes like this. Sign in to Google Ads, and when it pushes you toward creating a campaign, look for the option to switch to Expert Mode and then to create an account without a campaign. Depending on your country and account, Google may ask for billing details before setup finishes. You aren't charged for research, but don't launch a campaign by accident. Google moves these buttons around, so if your screen looks different, hunt for the same options rather than giving up.

Once you're in, open Tools, then Planning, then Keyword Planner, and choose "Discover new keywords." Type in your seeds. Set the location and language to match your real readers, not just wherever you happen to live. Those numbers can be very different from country to country.

There's a catch. Without active ad spend, Google tends to show volume as wide ranges, like 1K to 10K, instead of exact figures. The SEO Engine blog tested the free tier against four paid tools across 47 keywords and reached the same conclusion. The ranges are real, and they limit how precisely you can compare competitors. It's annoying. It's still useful, because it tells you whether a keyword lives in the hundreds or the tens of thousands, and that's often all a beginner needs.

Keep in mind that the tool was built for advertisers, so its suggestions lean commercial. The CPC column shows what advertisers pay per click. It can be a clue that advertisers see commercial value in a query, but it isn't a direct measure of what the searcher wants, so don't read it as "these people are ready to buy."

If you want a second set of numbers, the Keyword Surfer Chrome extension shows rough volumes on the Google results page, and Ahrefs has a free keyword generator with limited results. When the tools disagree, and they will, treat every figure as a direction and skip the hunt for the one true number.

Step 4: Check whether interest is rising or fading

A keyword with decent volume can still be a bad bet if fewer people search it every year. Google Trends catches that.

Type your keyword in and set the range to five years. Trends doesn't show search volume. It shows relative interest on a scale of 0 to 100, which is fine, because you're looking at the shape of the line, not the number.

Is it climbing, flat, or sliding? Does it spike at the same time every year? Check the chart yourself before you build a publishing plan around a guess.

Two coffee examples worth knowing. A QuestionDB guide on using Trends draws the line between a recurring pattern and a fad with two drinks. Searches for hot chocolate recipes jump reliably every winter. Dalgona coffee, the whipped coffee that took over social media in 2020, peaked and then faded fast. If you were planning a post around each, the first is a topic you can update and republish every year. The second is a bet that may already be over.

Once you know a topic is seasonal, timing matters. The UK agency Pod Digital gives a simple rule: if a keyword peaks in June, publish in April, so Google has time to crawl and index your page before demand arrives. Search Engine Land's guide to SEO seasonality makes a similar point, that you want to be visible before traffic peaks, not chasing it afterward. One caution from the same guides: seasonal terms can get crowded when the peak arrives, so check who's already ranking.

Trends also lets you compare terms side by side. If you can't decide between "cold brew concentrate" and "cold brew ratio," put both in and look at which one has more life in it.

Small or very specific keywords sometimes show "not enough data." That doesn't mean nobody searches them. It just means Trends can't say.

Step 5: Look at the competition with your own eyes

Paid tools give you a difficulty score. Free tools mostly don't, and those scores are rough guesses anyway. A quick manual check tells you a lot.

Search your keyword and study page one. Ask yourself a few things.

Who is ranking? If it's mostly huge publishers and big brands, you'll probably struggle for a while. Forum threads or small personal blogs in the results can be a sign that the page isn't fully locked up by major sites. That's worth investigating, not a green light. You still have to compare how relevant, thorough, fresh and well-linked those pages are next to what you could produce.

What kind of pages are they? This is the intent check. If page one is all product listings, a how-to guide won't fit. If it's all long guides, a thin 400-word post won't compete. Nullstacks, an SEO agency blog, describes the mistake as writing from the keyword down instead of from the results page up. The keyword tells you the topic, and the results page tells you the shape of page Google expects to show.

How old and how good is it? Outdated or shallow results can be an opening, though not a guarantee.

Write a quick note in your sheet: easy, medium, or hard. You're allowed to be wrong. You'll get better at this fast.

Step 6: Mine your own data in Search Console

If your site has been live for a while, Google Search Console is probably the best free keyword tool you have, because the data is yours. It shows real searches that made your pages appear on Google.

Verify your site, open the Performance report, and look at the Queries tab. Sort by impressions. Queries where you're already getting impressions but few clicks, or where you appear somewhere around positions 8 to 20, are worth a closer look. Google's own Search Console Help explains why to treat position carefully. It's an average across all the searches where you appeared, so it can differ from what you see when you search the phrase yourself.

A story from an agency. In a collection of Search Console success stories on Grit Daily, the team behind SERPpro describes opening its Performance report and finding that people were reaching the site through queries like "white label link building services" and "white label SEO partnerships." Those were more specific than the keyword the site had been targeting. The team built a separate page for each, because the intent behind each phrase was a little different, and says most of the new pages were ranking first or second within a week. Another contributor in the same piece describes starting by filtering for queries on page two, positions 11 to 20, with decent impressions and almost no clicks. Both are self-reported results, so don't expect the same speed. The lesson is the habit: your own query data often shows what people already think your site is about.

If your site is brand new, this report will be empty or close to it. That's normal. Verify the site now so the data starts collecting, do steps 1 to 5 in the meantime, and come back in a month or two.

Step 7: Choose, then write it down

Your sheet is probably messy by now. Keep the columns simple: the keyword, what the searcher really wants, the rough volume range, how crowded page one looked, and a status (idea, drafting, published). Fancy sheets don't get used.

Then pick using three questions. Is the intent clear? Does page one look beatable? Do you actually know enough to write something better than what's there?

If your site is new, lean toward long, specific phrases. Ahrefs makes this case with the example of someone launching a cryptocurrency blog. The popular, high-volume keywords are tempting, but a brand-new site has almost no chance of ranking for them soon. Longer, more specific phrases give even a new site a shot at the top ten, and ranking for one of them often brings visits from related searches too. For the coffee blog, skip "cold brew" and go for "how long does cold brew last in the fridge." Fewer people search it, but fewer sites are fighting for it.

A small-blogger version of the same lesson comes from the MostlyBlogging site. Its author says that many of her earliest posts used a single generic word as the keyword, and that she wished she had known about long-tail keywords when she started. She went back and updated the old posts once she did.

Each post gets one main keyword. If you cover the topic thoroughly, you'll naturally use many related terms without forcing them in. A keyword repeated awkwardly every third sentence reads like exactly what it is. Google Search Central's guidance on creating helpful, people-first content is worth reading once in full.

Mistakes that waste the most time

Chasing the biggest number is the classic one. A keyword with 50,000 monthly searches sounds great until you see who owns page one. A keyword with 300 searches that you could actually rank for can send you more traffic than the big one ever will.

Ignoring intent comes next. Volume means nothing if your page doesn't match what people want when they click. The River Pools story is the reverse of this mistake: its most valuable posts targeted questions from people close to buying.

Then there's treating estimates as facts. Two tools can give you very different numbers for the same keyword, and both are guessing. If you catch yourself agonizing over 1,200 versus 1,900, stop. Those two keywords are basically the same size.

Doing research once and never returning is quieter but just as costly. Search habits drift and new questions appear, so a short revisit every few months is plenty.

And writing for the tool instead of the reader. If a post reads oddly because you forced a phrase in, cut the phrase.

How long this takes

For a first pass on one topic area, give yourself an afternoon. Maybe two hours if you already know the subject well. After a few rounds it drops to under an hour, because you stop second-guessing every step.

It also gets faster to read results pages. After you've looked at enough of them, you start noticing mismatches, like a query that clearly wants a how-to while most of the top results are product pages, without opening every link.

Questions people ask

Can I do keyword research without knowing search volume?
Yes. Autocomplete, People Also Ask, Reddit threads and Search Console all show you what people care about without a number attached. Marcus Sheridan's whole approach started with customer questions, not volume data. Volume helps you compare ideas. It isn't required to find them.

Should I target keywords that show zero or very low volume?
Sometimes, yes. Tools often show low numbers for specific, newer, or conversational searches that real people do type. If the topic fits your site and you can answer it well, a low reported number alone isn't a reason to skip it.

How do I choose between two keywords with the same intent?
Look at both results pages. Pick the one where the top pages feel weaker or more dated, or where you know something the current pages don't cover. If they look equal, write about the one you can explain better.

Is Google Keyword Planner really free?
You don't pay to use it, but you need a Google Ads account, and without ad spend you usually see ranges instead of exact numbers.

Do I need a website to start?
Steps 1 to 5 don't need one. Only Search Console does.

Is AI-assisted writing a problem for search?
Google's published guidance on AI-generated content says the focus is on quality and helpfulness, not on how the content was produced.

Sources for the stories and facts above

The River Pools and Spas story comes from PRNEWS's coverage of Marcus Sheridan's Content Marketing World keynote and from his interview on the Find Your Yellow Tux podcast. The intent example comes from the Spiderworking blog's summary of his talk. The Keyword Planner range findings come from The SEO Engine blog. The Trends examples come from QuestionDB's guide to Google Trends for keyword research, Pod Digital, and Search Engine Land's guide to SEO seasonality. The Search Console story comes from Grit Daily's collection of Google Search Console success stories, and the explanation of average position comes from Google's Search Console Help. The long-tail examples come from Ahrefs and the MostlyBlogging site, and the results-page point comes from the Nullstacks blog. Google Search Central's helpful-content and AI-content guidance is referenced in the final sections. Figures and outcomes are as reported by those sources and may not match your own results.

READ HERE:

How to Do Keyword Research for Free: A Step-by-Step Guide for Beginners - TopBlogs

Learn keyword research for free with Google Search Console, Keyword Planner, Trends and autocomplete. A simple process for beginners, with real stories from other sites.

topblogs.online

Creem.io for Pakistani Developers: A Practical Integration Guide (2026)

TheBitForge — Mon, 28 Sep 2026 14:09:08 +0000

READ HERE

Creem.io in Pakistan: Payouts, Fees & Next.js Integration - TopBlogs

Does Creem.io work in Pakistan? Learn about payouts, fees, restrictions, Merchant of Record, and how to integrate Creem with Next.js and webhooks.

topblogs.online

Picture this. You've spent four months on a small SaaS tool. It works, your friends say it's good, and a stranger in Germany has just asked where they can pay for it. You open Stripe, start the signup, and Pakistan isn't in the country list.

That moment is where a lot of Pakistani developers stall. Some give up. Some pay for a US LLC they don't really want. Some send customers a Payoneer link and hope for the best.

Creem.io is one of the newer platforms trying to fix that gap, and it does list Pakistan as a supported country. But "supported" hides a few details that decide whether it works for you. This guide walks through those details first, then builds a full integration with real code.

Last checked in September 2026. Payment rules change quickly, so confirm anything money-related on Creem's own docs before you commit. This article is independent and isn't sponsored by Creem.

What Creem actually does

Creem is what the industry calls a Merchant of Record. The term sounds heavier than it is.

Think of a shop that sells your product for you. When a customer in France buys, the shop is the one legally selling to them. It charges the card, works out the French VAT, sends the invoice, and deals with the bank if the customer disputes the charge. Then it pays you what's left after its cut. With a normal payment processor like Stripe, you are the seller, so all of that lands on you.

Creem is built by Armitage Labs OU, an Estonian company, and it targets software and digital goods. The API covers products, hosted checkout pages, customers, subscriptions, discount codes, and license keys. If you sell a SaaS plan, an extension, a template pack, or an ebook, you're the intended customer.

So, does it work in Pakistan?

Yes. Pakistan is on Creem's list of supported merchant countries, and buyers in Pakistan can purchase from you too, since Pakistan isn't on the unsupported list.

Now the catch. On that list, Pakistan carries a double asterisk. The docs say countries with that mark may face restrictions from Creem's bank transfer partner, for example only personal bank accounts being accepted, or business transfers being unavailable.

The partner is Wise, and Wise's own help page for PKR transfers is blunt. It says you can send PKR to personal bank accounts in Pakistan, that you can't send to a business account, and it warns against a couple of account types, namely Meezan Bank Express and Allied Bank Express accounts, which reject these transfers.

Put those two facts together and you get a fairly clear picture.

If you're a freelancer or solo founder, onboarding as an individual with a personal PKR account in your own name is the path most likely to go smoothly. The name on the bank account has to match the name on your identity verification, because a mismatch is one of the documented reasons a payout bounces back.

If you run a registered company and want the money to land in a company account, don't assume it works. Email Creem support first and ask directly. It's a five-minute question that can save you weeks.

There's also a backup route. Creem pays out in USDC on the Polygon network for a 2% fee. If your bank keeps rejecting transfers, that's your escape hatch, though you'd need a wallet and a way to turn USDC into rupees that you trust.

What you can sell, and what will get you turned away

This part surprises people, so read it before you spend a weekend on integration.

Creem is for digital products. Its account review rules say services of any kind aren't accepted, and the examples they give include marketing, design, web development and consulting. So if your plan is to collect payment for client projects, Creem isn't the tool. That's a real limit for the many Pakistani developers who earn through freelancing.

Generative AI products, such as text-to-image or text-to-video tools, sit on a restricted list. Restricted doesn't mean banned. It means extra checks, and they'll ask about your previous payment processor, your refund rate, and why you're moving.

One more thing. Be honest about what you sell. Creem can ask for test access to your product and may make a small test purchase during onboarding. Sellers who hide things tend to get their accounts closed, and closed accounts with money inside are a miserable problem.

What it costs, in actual numbers

The headline rate is 3.9% plus $0.40 per successful transaction, with no monthly fee. On a $29 sale with no tax added on top, that's about $1.53 gone to the platform.

Some features cost extra. Revenue splits and the affiliate system each add 2%, and abandoned-cart recovery adds 5% on the recovered sale. If you don't turn those on, they don't touch you.

The number that matters most for someone in Pakistan is the payout fee, which is $7 or 1% of the payout, whichever is bigger. Run it on a few amounts and the trap becomes obvious.

Withdraw $100 and you lose $7, which is 7% of your money. Withdraw $700 and 1% comes to exactly $7, so that's the break-even. Withdraw $2,000 and the fee is $20, or a clean 1%.

So don't withdraw just because the 15th is coming. Let the balance build and take it out in bigger lumps. The minimum to request a payout is $50, and payouts run on the 1st and the 15th of each month.

Two timing details are easy to miss. New payments can be held for 7 to 12 days for risk review, which means a sale you make on the 12th usually won't be ready for the 15th. And if your bank account currency differs from the currency you charged in, the bank partner applies its own conversion fee, which Creem says it doesn't control.

Where I'd be cautious

A competitor's comparison post criticizes Creem's extra fees and says checkout supports only USD and EUR. That source sells a rival product, so treat it with salt, but the currency point is easy to verify yourself in the dashboard before you price anything.

There's also a small inconsistency worth knowing about. Creem's pricing page says automatic tax collection covers 50+ countries, while its homepage talks about compliance across 190+ countries. They may be describing different things, but if one particular market matters to you, ask support whether it's covered.

Trustpilot reviews lean positive on support and documentation, with some complaints about individual merchants' billing. That's normal for any payments company. Just don't rely on marketing pages alone.

Building the integration

I'll use Next.js and the official TypeScript SDK because that's what most people reach for. Creem also has a REST API, a Better Auth plugin, and a Convex component if your stack looks different.

Create your account and a test product

Sign up at creem.io, finish verification, then flip the Test Mode toggle at the bottom of the left sidebar. Everything in test mode is walled off from real money, which is exactly what you want while you're making mistakes.

Open the Products tab and create your first product. Set the name, the price, and whether it's one-time or recurring. Prices are stored in cents, so 1000 means $10.00. Choose a tax category (SaaS, digital goods, or ebooks) and a tax mode, inclusive or exclusive. Copy the product ID that starts with prod_. Then head to the Developers section and copy your API key.

Set up the environment

npm install creem

# .env.local
CREEM_API_KEY=your_test_api_key
CREEM_WEBHOOK_SECRET=whsec_your_webhook_secret
CREEM_ENV=test
NEXT_PUBLIC_APP_URL=http://localhost:3000

The API key belongs on the server only. Don't put it in browser code and don't commit it to Git. Test and live modes use different keys and different base URLs, https://test-api.creem.io for testing and https://api.creem.io for production.

Create the client

// lib/creem.ts
import { Creem } from "creem";

export const creem = new Creem({
  apiKey: process.env.CREEM_API_KEY!,
  ...(process.env.CREEM_ENV !== "production" && { server: "test" as const }),
});

When you leave server out, the SDK talks to production. This setup only forces test mode when you haven't said you're in production, which is a safer default than the reverse.

Create a checkout session

// app/api/checkout/route.ts
import { NextRequest, NextResponse } from "next/server";
import { creem } from "@/lib/creem";

export async function POST(request: NextRequest) {
  try {
    const { productId, userId, email } = await request.json();

    const checkout = await creem.checkouts.create({
      productId,
      successUrl: `${process.env.NEXT_PUBLIC_APP_URL}/success`,
      customer: { email },
      metadata: { userId },
    });

    return NextResponse.json({ checkoutUrl: checkout.checkoutUrl });
  } catch (error) {
    console.error("Checkout error:", error);
    return NextResponse.json(
      { error: "Could not create checkout" },
      { status: 500 }
    );
  }
}

That metadata line is easy to skip and painful to regret. Put your own user ID in it. When the webhook fires later, the same metadata comes back, so you know exactly which account in your database just paid.

On the front end, call the route and send the browser to the URL it returns.

async function buy() {
  const res = await fetch("/api/checkout", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      productId: "prod_YOUR_PRODUCT_ID",
      userId: currentUser.id,
      email: currentUser.email,
    }),
  });
  const { checkoutUrl } = await res.json();
  window.location.href = checkoutUrl;
}

Handle webhooks

This is the part that actually decides whether your app gets paid correctly.

Don't unlock anything just because someone reached your success page. Anyone can type that URL into a browser. The webhook is the only trustworthy signal that money moved.

Creem signs each webhook. The signature arrives in the creem-signature header, and it's an HMAC-SHA256 of the raw request body using your webhook secret. That's why you read the body as raw text before doing anything else with it.

// app/api/webhooks/creem/route.ts
import { NextRequest, NextResponse } from "next/server";
import { constructWebhookEventEntity } from "creem/webhooks";

export async function POST(request: NextRequest) {
  const rawBody = await request.text();

  const event = await constructWebhookEventEntity(rawBody, request.headers, {
    secret: process.env.CREEM_WEBHOOK_SECRET!,
  }).catch(() => null);

  if (!event) {
    return NextResponse.json({ error: "Invalid signature" }, { status: 401 });
  }

  // The same event can arrive more than once, so skip repeats.
  const eventId = JSON.parse(rawBody).id as string;
  if (await hasProcessed(eventId)) {
    return NextResponse.json({ received: true });
  }

  switch (event.eventType) {
    case "checkout.completed": {
      const userId = event.object.metadata?.userId;
      // Unlock the one-time purchase for this user.
      break;
    }
    case "subscription.paid": {
      // Activate or renew access for the paid period.
      break;
    }
    case "subscription.past_due":
    case "subscription.unpaid": {
      // Show a payment-recovery message and restrict access per your policy.
      break;
    }
    case "subscription.canceled": {
      // Revoke access.
      break;
    }
  }

  await markProcessed(eventId);
  return NextResponse.json({ received: true });
}

hasProcessed and markProcessed are stand-ins for your own database. A table with the event ID as a unique column is plenty.

A few details about how Creem behaves. Answer with HTTP 200 when you've handled an event. If you don't, Creem tries again, five attempts in total, after 30 seconds, 5 minutes, 30 minutes and 6 hours, and it stops after 24 hours. Its docs suggest using subscription.paid to switch on access and keeping subscription.active for syncing. Creem also doesn't publish fixed IP addresses for webhooks, so an IP allowlist won't protect you. The signature check is your protection.

If you'd rather skip the SDK helper, you can verify the signature by hand.

import crypto from "crypto";

function isValidSignature(rawBody: string, header: string, secret: string) {
  const expected = crypto
    .createHmac("sha256", secret)
    .update(rawBody)
    .digest("hex");

  const a = Buffer.from(expected);
  const b = Buffer.from(header ?? "");
  return a.length === b.length && crypto.timingSafeEqual(a, b);
}

Test it locally

Creem needs a public HTTPS address to reach. While you're developing, the Creem CLI can forward events straight to your machine.

creem listen --forward-to http://localhost:3000/api/webhooks/creem

If you prefer a tunnel like ngrok, register that URL under Developers and then Webhooks in the dashboard. Add your production URL separately once you deploy.

In test mode, the card 4111 1111 1111 1111 gives you a successful payment with any future expiry and any CVC. The card 4507 9900 0000 0028 simulates a decline, and 4507 9900 0000 0010 simulates insufficient funds. Try the failing cards on purpose. Subscriptions especially tend to break in the unhappy paths, not the happy one.

Go live

Before you switch, walk through this once.

Run every flow in test mode: a purchase, a failed payment, a cancellation, a refund.
Swap in your live API key and drop the test server setting.
Recreate your products in live mode, because test products don't carry over.
Register your production webhook URL and copy the live webhook secret.
Add your payout account and finish identity verification.
Watch your first handful of real transactions closely.

Getting paid in Pakistan

Set up your payout account long before you need it. For a bank payout, expect to give your full name and your IBAN, which is what Wise asks for on PKR transfers. Use a personal account in your own name, and stay away from the Express account types mentioned earlier.

If a payout fails and the money returns to your balance, it's usually one of three things. The bank details are wrong. The account can't receive international payments. Or the account holder's name doesn't match the identity you verified with. Check those before opening a support ticket, because you'll likely find the answer yourself.

Mistakes that tend to bite

A few come up again and again with payment integrations, and Creem is no exception.

Granting access from the success page is the big one. It feels fine in testing and then someone shares the URL. Use the webhook.

Parsing the request body before verifying the signature is another. Once a framework re-serializes the JSON, the bytes change and the signature check fails for no obvious reason. Read the raw text first.

Mixing environments causes the strangest errors. A test key pointed at the production endpoint, or the other way round, fails in ways that look like bugs in your own code. Check your environment variables before you start debugging logic.

And then there's the small, quiet one. Withdrawing $60 because you're impatient and losing $7 of it. Wait a month.

Your own taxes

Creem, as Merchant of Record, handles sales tax and VAT on what your customers buy. It doesn't handle your income tax in Pakistan. Those are separate worlds.

Each payout comes with a reverse invoice, which is useful paperwork, so download and keep them along with your bank credit records. How foreign income is taxed for freelancers and software exporters in Pakistan has its own rules and they change over time. Talk to a qualified accountant instead of trusting a blog post, this one included.

Who should use it

Creem makes sense if you sell a SaaS product, browser extension, template pack, ebook or licensed software, if a personal bank account in your own name works for you, and if you'd rather not learn how to file taxes in a dozen jurisdictions.

It's the wrong tool if most of your income is client work, if you need money to land in a business account, if you need to be paid more often than twice a month, or if your product sits in a restricted category.

Whichever way you lean, test with something small. List a cheap product, complete one real sale, and follow that money all the way into your bank account before you build anything bigger on top. Watching your own first payout arrive will tell you more than any review, including this one.

Frequently asked questions

Is Creem available in Pakistan?
Yes. Pakistan is on the supported merchant country list, with a note that bank partner restrictions may affect payouts.

Can customers in Pakistan buy from a Creem checkout?
Purchases are accepted from every country except those on Creem's unsupported list, and Pakistan isn't on it. Which payment methods appear depends on the buyer's location, the product type, the price and the device.

Do I need a registered company?
No. Creem accepts individual sellers, who go through standard identity checks. Individuals need a payout account in the same name as their verified identity.

How does the money reach me?
Through a local bank transfer handled by Creem's bank partner, or through USDC on Polygon. Payouts run on the 1st and 15th of each month.

What's the minimum payout?
Your balance has to reach $50 or 50 EUR before you can request a withdrawal.

Can I collect payment for freelance projects with it?
No. Services such as consulting, design and web development aren't accepted. Creem is designed for digital products.

Does it work with Next.js?
Yes. There's an official TypeScript SDK and a dedicated Next.js adapter, plus a Better Auth plugin and a Convex component.

Sources and last checked

Everything factual above was checked in September 2026 against these official pages: Creem's Supported Countries, Payouts, Quickstart, Test Mode and Webhooks documentation, its Pricing page, Terms of Service and Account Reviews policy, and Wise's help article on PKR transfers. Rules on all of these can change, so double-check current details before you make decisions.

Disclaimer: This article is general information, not financial, legal or tax advice.

DEV Community: TheBitForge

Best Stock Market APIs for Python Developers in 2026

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roicai / roicai-python

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ROIC.AI Python Library

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